Genetic algorithms have been successfully applied to a wide variety of problems . Although widely used , there are still some shortages . This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process . This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic‐based algorithm . This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features . In this study , a novel fast genetic algorithm was proposed for constrained multi-objective optimization problems . The handling of constraint conditions were distributed to the initial population generation and each genetic process . Combine the constraint conditions and objectives , a new partial-order relation was introduced for comparison of individuals . It is well known that a judicious choice of crossover and/or mutation rates is critical to the success of genetic algorithms . Most earlier researches focused on finding optimal crossover or mutation rates , which vary for different problems , and even for different stages of the genetic process in a problem . In this paper , a generic scheme for adapting the crossover and mutation probabilities is proposed . Genetic algorithms ( GA 's ) are adaptive methods that may be use to solve search and optimization problems . They are based on the genetic processes of biological organisms . Over many generations , natural populations evolve according to the principles of natural selection and " survival " of the fittest . A method of self-adaptive mutation , crossover and selection was implemented and applied in four genetic algorithms . So developed self-adapting algorithms were then compared , with respect to convergence , with a traditional genetic one , which contained constant rates of mutation and crossover . The experiments were conducted on six benchmark functions including two unimodal functions , three multimodal with many local minima , and one multimodal with a few local minima . This work deals with the optimization of the decoupling capacitors of a power delivery network by using a nature-inspired algorithm . In particular , the capacitance value and the location of three decoupling capacitors are optimized in order to obtain an input impedance below a specific mask , by using a nature-inspired algorithm , the genetic one , in combination with two electromagnetic solvers used to compute the objective function . An experimental board is designed and manufactured ; measurements are performed to validate the numerical results . Autonomousrepairand refurbishment of reprogrammable logic devices usingGenetic Algorithms canimprove thefault tolerance ofremote mission-critical systems . Thegoalofincreasing availability byminimizing therepair timeisaddressed inthis paperusing a CGTprunedGenetic Algorithm . For each module defining the path- predicate representation with an expression in disjunction normal form . This expression is then used to create a genetic template ( schema ) in a special genetic system model . The secondary aim of this work was to construct a prototype system designed and implemented to employ a specially designed Case Based Management and Retrieval Mechanism and also to provide maintenance of the case library . A success ( risk ) measure of guaranteed availability is proposed . Using a genetic system model , the authors describe the measure and study the effects of the guaranteed level and the observation period on it . Furthermore , they introduce a numerical approach for continuous-time Markov chain models which allows component-level modeling , Coxian failure and repair distributions , time-dependent failure and repair rates and deferred repair and nondeferred repair strategies to be handled . From the reviews about implementation HOLAP with Genetic Algorithm approach was expected being used as a basis research on HOLAP implementation for query optimization on distributed database with genetic algorithm approach . Keyword s : Query Optimization ; Distributed database ; HOLAP ; OLAP ; Genetetic algorithm . The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts . Furthermore , the probability of falling into local minima is reduced , because genetic the algorithm is used to optimize the neural network system . Abstract Computational cost poses a major obstacle to the design of indoor environments with the current optimal method and computational fluid dynamics ( CFD ) . A novel optimization method integrating a genetic algorithm ( GA ) , an artificial neural network ( ANN ) , multivariate regression analysis ( MRA ) , and a fuzzy logic controller ( FLC ) was proposed in this paper to optimize the indoor environment and energy consumption based on simulation results . Thermal comfort ( predicted mean vote ) was set as the restrictive design objective . Genetic algorithm ( GA ) is one of the standard solutions to solve many optimization problems . One of a GA type used for solving a case is island model GA ( IMGA ) . Localization strategy is a brand-new feature for IMGA to better preserves its diversity . The intrinsic strength of population based solution of GA offers asuperior advantage for this type of scheduling problems . Simulation results demonstrate the effectiveness of GA based scheduling scheme compared with the well-known M-LWDF scheduling rule and the simulated annealing method . It can offer better video quality at the receiver within the required delay bound . The algorithm is suitable for blind spectrum sensing where the properties of the signal to be detected are unknown . The GA optimized SVD gives better result in the low signal to noise ( SNR ) environment . The objective function is to minimize the amount of load shed while maximizing the lowest swing frequency at all stages . To validate the proposed GOA-based UFLS , a comparison with adaptive , particle swarm optimization ( PSO ) and genetic algorithm ( GA ) UFLS is carried out for different disturbances . Two different case study systems are considered to test the accuracy and reliability of the proposed algorithm : the IEEE 9-bus and 39-bus systems . Abstract Genetic Algorithm ( GA ) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive . A review of the literature shows extensive research attempting to adapt and develop the standard GA . Ten volunteers have participated of these studies , in which five participants were experienced designers and the remaining five were non-experienced designers of analog CMOS ICs . Our experimental results have shown that the iGA is capable of reducing the OCT in about 86 % in relation to the evolution process by using a non-interactive standard GA . More interestingly , both volunteers ' teams have obtained similar OCTs and OTAs ' robustness , indicating that iGA might be helpful not only to avoid the common optimization issue of local minima due to the human interaction with the computational tool ( iMTGSPICE ) , but also it is capable of producing similar results in terms of OCTs and OTA 's robustness . In this way , the adapting for operators prevents its premature convergence and the adapting in the range of coverage causes maximum use of the problem ’s important resources and better performance of the algorithm in each step of its run . In the proposed GA ( GAIA ) , initially the adapting in the amount of algorithm ’s operators based on the solutions ’ tangent rate for premature convergence is done , then the adapting in the range of coverage of operators ’ algorithm , in first step , happens by operators convergence on BR ( which was detected initially ) and , in the next step , occurs by operators convergence on the elite solutions so that the search process focuses on more probable ranges than the whole range of solution . Comparing the problem results in the static state with the results of other available methods in the literature indicated high efficiency of the proposed method . When GA is searching test data for a particular target branch heuristically , it may reach the other target branches , if so happen , then those test data will get stored into the buffer space hence it is not necessary to run GA to cover that branch . Thus the Buffered Genetic Algorithm ( BGA ) approach outperforms the other GA based automated test data generation approaches in terms of number of iterations and search effectiveness . The proposed approach employs control flow graph to traverse and predicate the branch coverage . Unfortunately , these parameters interact with each other in a complicated way and determining which parameter set is best to use for a specific problem can be a complex task requiring much trial and error . This inherent drawback is overcome in this paper where it presents two adaptive GA approaches to HLS , the adaptive GA operator probability ( AGAOP ) and adaptive operator selection ( AOS ) and compares the performance to the standard GA ( SGA ) on eight digital logic benchmarks with varying complexity . The AGAOP and AOS are shown to be far more robust than the SGA , providing fast and reliable convergence across a broad range of parameter settings . Softmax ( 5 ) Here , by CFS we denote a convolutional layer with F filters each of size S , by SP#S a spectral pooling layer with output dimensionality S , and GA the global averaging layer described in Lin et al . ( 2013 ) . In this paper we study the problem of answering cloze - style questions over documents . Our model , the Gated - Attention ( GA ) Reader , integrates a multi - hop architecture with a novel attention mechanism , which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader . This enables the reader to build query - specific representations of tokens in the document for accurate answer selection . They show state - of - the - art language modeling and parsing performance . We investigate what information they learn , from a linguistic perspective , through various ablations to the model and the data , and by augmenting the model with an attention mechanism ( GA - RNNG ) to enable closer inspection . We find that explicit modeling of composition is crucial for achieving the best performance . Head rules , conversely , are analogous to giving all attention to one constituent , the one containing the lexical head . We now formally define the GA - RNNG ’s composition function . Recall that is the concatenation of the vector representations of the RNNG ’s data structures , used to assign probabilities to each of the actions available at timestep ( see Fig . If the endocentric hypothesis is true ( that is , the representation of a phrase is built from within depending on its components but independent of explicit category labels ) , then the nonterminal types should be easily inferred given the endocentrically - composed representation , and that ablating the nonterminal information would not make much difference in performance . Specifically , we train a GA - RNNG on unlabeled trees ( only bracketings without nonterminal types ) , denoted U - GA - RNNG . This idea has been explored in research on methods for learning syntax with less complete annotation . If endocentricity is largely sufficient to account for the behavior of phrases , where do our robust intuitions for syntactic category types come from ? We use t - SNE to visualize composed phrase vectors from the U - GA - RNNG model applied to the unseen test data . Fig . Comparing with the AS Reader , ReasoNet shows the signi ca nt improvement by capturing multi - turn reasoning in the paragraph . Iterative Attention Reader , EpiReader and GA Reader are the three multi - turn reasoning models with xed reasoning steps . ReasoNet also outperforms all of them by integrating termination gate in the model which allows di erent reasoning steps for di erent test cases . Consequently , the deployment optimization problem is abstracted into a combinatorial optimization problem . Then optimization model including the graph structure , cost function and heuristic function for the algorithm is built to meet the requirement of the heuristic search algorithm , and its correctness is also strictly proofed . Finally , experiment in real city area with occlusions ( such as building ) validates the usability of our method . This paper improves the laplacian method and achieves the mid-result which contains whole road information . Based on image from ADS40,we tried the heuristic searching procedure to extract road target from the mid-result . And the experiment indicates that this method is effective and reaches the expected purpose . It is the process of determining whether two systems are equivalent to each other according to some mathematically deflned notion of equivalence . In this paper to improve e‐ciency we present a procedure , based on heuristic searches , for checking the well-know simulation relation deflned by Milner . We use heuristic mechanisms for the exploration of the search space , in order to avoid the construction of the complete state graph . To improve localization accuracy , we define an evaluation feedback metric to quantify the estimation errors and formulate jammer localization as a nonlinear optimization problem , whose optimal solution approaches jammer 's true position . We exploit a heuristic search based algorithm for approximating the global optimal solution , and our extensive simulation shows that our error-minimizing-based algorithm outperforms existing algorithms . Our work applies to A*-based HTN planners with lifting . We prove that the planner finds the plan of maximal expected utility , while retaining its lifting capability and efficient heuristic-based search . We show how to learn the probabilities online , which allows a robot to adapt by replanning on execution failures . With the tumultuous increase in acceptance of service oriented architecture , and thus , a rise in the number of web services , skilled software developers spend a lot of time composing web service workflows , rather than creating innovative and efficient services . Hence , we put forward a technique of code reusability that utilizes heuristic based search methods to automate service workflow composition by weighting quality of service criteria by relevance and importance to the users . We implement a novel and heuristic-based graph creation and search algorithm where the heuristic function value is calculated through the uniform cost search based on each of the quality of service criteria specified by the user . Abstract Different schemes for the parallelization of a new algorithm to accelerate the Ray-Tracing for computing the electrical field radiated from or scattered by complex objects using high frequency techniques are presented . This algorithm is based on a combination of the Angular Z -Buffer ( AZB ) , the Space Volumetric Partitioning ( SVP ) and the A*Heuristic search method . The algorithm is very useful for different applications . A combination of several ray-tracing acceleration techniques for computing very efficiently the radar cross section ( RCS ) of complex targets has been implemented in a new computer tool . The AZB and SVP algorithms together with the A * heuristic search method have been developed to consider multiple iterations between different flat surfaces that compound the target . Traditional project scheduling and optimization methods have been unable to fully meet the rapid development of modern project management needs . The PSO ( Particle Swarm Optimization ) is a simulation of birds the heuristic search algorithm mechanisms , which function optimization , constrained optimization , minimax problems , such as multi-objective optimization problem . It has become an important branch of the many related optimization fields . The main objective of the present work was to propose a small-hole EDM-drilling methodology using the heuristic search techniques to calculate the optimal toolpoint path that minimizes the small-hole EDM-drilling total machining time . Firstly , the tool points of the small-hole EDM-drilling are formulated as a traveling salesman problem ( TSP ) , and a new heuristic search technique algorithm of guided fast local search ( GFLS ) is proposed ; to find the optimal small-hole EDM-drilling toolpoint path . This optimal toolpoint path ; is the path that passes through all toolpoints and has the minimum machining time . That is , the model is trained to minimize . Once the decoder is trained , discrete sequence generation can be performed by approximately maximizing the probability of the target sequence under the conditional distribution , , where we use the notation to emphasize that the decoding process requires heuristic search , since the RNN model is non - Markovian . In practice , a simple beam search procedure that explores prospective histories at each time - step has proven to be an effective decoding approach . In recent years there has been much interest in the Monte Carlo tree search algorithm , a new , adaptive , randomized optimization algorithm . In fields as diverse as Artificial Intelligence , Operations Research , and High Energy Physics , research has established that Monte Carlo tree search can find good solutions without domain dependent heuristics . Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy . We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree . In this work , we present a MCTS-based Go-playing program which uses convolutional networks in all parts . Our method performs MCTS in batches , explores the Monte Carlo search tree using Thompson sampling and a convolutional network , and evaluates convnet-based rollouts on the GPU . We achieve strong win rates against open source Go programs and attain competitive results against state of the art convolutional net-based Go-playing programs . Due to its simplicity of the rules and high degree of difficulty , Ms. Pac-Man has become an at- tractive subject of research on digital game AI . In Ms. Mac-Man , Monte Carlo Tree Search-based , especially UCT-based players tend to give better performance than rule-based systems . In this study , we propose the improvement of Monte-Carlo tree search using the evaluation function that have been learned in TD ( ) . Then , we formulate the energy-efficient task assignment problem by taking into account the necessary constraints . We next propose a Monte Carlo Tree Search based algorithm , named , TA-MCTS for the task assignment problem . Simulation results show that compared to four alternative benchmark solutions in literature , our proposal can reduce energy consumption up to 45.37 percent . We prove that standard no-regret algorithms can be used to learn optimal strategies for a scenario where the opponent uses one of these response functions . We demonstrate the effectiveness of this technique in Leduc Hold'em against opponents that use the UCT Monte Carlo tree search algorithm . We prove that this technique can produce optimal strategies for playing against pretty-good responses . We empirically demonstrate the effectiveness of the technique by finding static strategies that are strong against Monte Carlo opponents who learn by sampling our strategy , including the UCT Monte Carlo tree search algorithm . Presentation ID : PM_D32 Abstract : Not fully finalized , but I will examine how a Monte Carlo tree search algorith can be used in two particular finite games and the results . Furthermore , the proposed solution was implemented as a parallel adaptive neural network for a graphics processing unit that achieves a performance compatible with the ones obtained for deep learning methods . HearthBot win rate was evaluated in two experiments playing against a Monte Carlo tree search heuristic with competitive decks on a HearthStone simulator called Metastone . Results show that the proposed solution allows HearthBot to obtain an average win rate performance of 80 % against known decks and 70 % against unknown decks . In order to identify and select feasible container layouts that are practical and easy to load , a measure of disorder , based on the concept of entropy in physics and information theory , is derived . Finally , a novel method of directing a Monte-Carlo tree search process using the derived entropy measure is employed , to generate loading layouts that are comparable to those produced by expert human loaders . In summary , this thesis presents a new approach for dealing with real-world container loading in a warehouse environment , particularly in instances where layout complexity is of major importance ; such as the loading of heavy palletised goods using forklift trucks . It enables us to customize each portion of the Monte Carlo Tree Search algorithm , as well as gives us the possibility to monitor our simulations with several data visualizers . The simulation results have shown that a Monte Carlo Tree Search simulation can be a very challenging opponent in a game of Texas Hold'em . Unfortunately , its decision-making speed can not be compared to other algorithms with more domain specific knowledge . For most practical sequence generation tasks , e.g. machine translation , the reward signal is meaningful only for the entire sequence , for instance in the game of Go , the reward signal is only set at the end of the game . In those cases , state - action evaluation methods such as Monte Carlo ( tree ) search have been adopted . By contract , our proposed SeqGAN extends GANs with the RL - based generator to solve the sequence generation problem , where a reward signal is provided by the discriminator at the end of each episode via Monte Carlo approach , and the generator picks the action and learns the policy using estimated overall rewards . Thus , to evaluate the action - value for an intermediate state , we apply Monte Carlo search with a roll - out policy to sample the unknown last tokens . We represent an - time Monte Carlo search as where and is sampled based on the roll - out policy and the current state . In our experiment , is set the same as the generator , but one can use a simplified version if the speed is the priority . We also performed a set of experiments to investigate the robustness and stability of training SeqGAN . For future work , we plan to build Monte Carlo tree search and value network to improve action decision making for large scale data and in the case of longer - term planning . section : Acknowledgments The corresponding representation learning issue has some parallels to the representation problem that we address by using an autoecoder , but it is unclear how the appropriate metric for the prior methods can be acquired automatically on tasks with raw sensory input , such as the Atari games in our experimental evaluation . Methods based on Monte - Carlo tree search can also scale gracefully to complex domains , and indeed previous work has applied such techniques to the task of playing Atari games from screen images . However , this approach is computationally very intensive , and requires access to a generative model of the system in order to perform the tree search , which is not always available in online reinforcement learning . AlphaZero learns these move probabilities and value estimates entirely from self - play ; these are then used to guide its search . Instead of an alpha - beta search with domain - specific enhancements , AlphaZero uses a general - purpose Monte - Carlo tree search ( MCTS ) algorithm . Each search consists of a series of simulated games of self - play that traverse a tree from root to leaf . The resolve of this research is planned as the first step of four-tier test-formatted HCCT development as one of investigative test instrument on Hydrostatic . The research method used the 4D model which has four comprehensive steps : 1 ) defining , 2 ) designing , 3 ) developing and 4 ) disseminating . The instrument developed has been tried to 30 students in one of senior high schools . Through several experiments we show that A4 can bypass AdGraph about 60 % of the time , which surpasses the state-of-the-art attack by a significant margin of 84.3 % ; in addition , changes to the visual layout of the web page due to these perturbations are imperceptible . We envision the algorithmic framework proposed in A4 is also promising in improving adversarial attacks against other learning-based web applications with similar requirements . We deduce the parameters that affect the DOF during the pickup process through theoretical analysis . In the modeling software C4D ( Cinema 4D ) the experiment is carried out . The results show that off-axis method is better , and verify the correctness of the calculation formula of DOF from the pickup process . The transfer inefficiency increases sharply as feature size is reduced but decreases as oxide thickness is reduced . The fabrication and testing of C4D 's with barrier lengths in the range 1.75 µm to 5 µm are described , and the measured transfer inefficiencies compare well with theoretical predictions . This R&D aims to develop e-modules based on blended learning in the cost esti-mating course . The research used the R&D method with the 4D ( four-D ) model carried out through four main stages , namely define , design , development , and dissemination . The e-module development was limited only to the assessment and limited dissemination to experts or the development stage . The paper describes development of a shoulder joint support equipment for the athlete to learn proper movement of his shoulder in such sports and games as baseball and tennis . A simple 4DoF model of the scapula , which can change position relative to the human body , is proposed . The scapula is attached to the human body via four or more muscles . In this paper , three sets of neural networks were trained respectively for three wavelengths:9,10 and 12 μm . Results of the experiment show that this fast calculation approach could calculate the top of atmosphere radiance with the error less than 0.1 K compared with 4 A , and its running speed is 100 times faster than that of 4 A for a single wavelength . This approach also enjoys more flexibility for the choosing of spectral channels . Unit information for 4A4 : Continuum Mechanics ( MATH7424 ) , a unit offered by the University of Western Australia . The application of multi-CCD splicing technology to a high resolution scanner is described . For the image scanning system developed with 4CCD technology , the designed physical resolution is 700 DPI , the resoluble interpotations are 350 DPI and 1400 DPI , the requirement of 0.08 m line width image is satisfied , the relative accuracy of image scanning is 0.1 mm , the horizontal splicing accuracy and vertical installation accuracy are controlled within a pixel , the corresponding object plane is 0.036 mm . Hence , the 4D-IS concept with its ability to capture a full image cube at a single exposure and provide real time data processing offers a new and enhanced capability over present sensors . The 4D-IS uses a reformatter fiber optics to map a 2D image to a linear array that serves as an input slit to an imaging spectrometer . The paper describes three such instruments , a VNIR , a MWIR , and a dual band MW/LWIR . This CONOPS utilises intermeshed Hosts ; entities and architectural design of the aerial corridor using ‘ 4 dimensional Security containment box ’ ( 4D – SCB ) . That is , a 4D – SCB ( axis X , Y , Z and time ) that requires a micro Radar network linked to AFCS — NCC .
Genetic algorithms have been successfully applied to a wide variety of problems . Although widely used , there are still some shortages .
This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process . This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic‐based algorithm . This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features .
In this study , a novel fast genetic algorithm was proposed for constrained multi-objective optimization problems . The handling of constraint conditions were distributed to the initial population generation and each genetic process . Combine the constraint conditions and objectives , a new partial-order relation was introduced for comparison of individuals .
It is well known that a judicious choice of crossover and/or mutation rates is critical to the success of genetic algorithms . Most earlier researches focused on finding optimal crossover or mutation rates , which vary for different problems , and even for different stages of the genetic process in a problem . In this paper , a generic scheme for adapting the crossover and mutation probabilities is proposed .
Genetic algorithms ( GA 's ) are adaptive methods that may be use to solve search and optimization problems . They are based on the genetic processes of biological organisms . Over many generations , natural populations evolve according to the principles of natural selection and " survival " of the fittest .
A method of self-adaptive mutation , crossover and selection was implemented and applied in four genetic algorithms . So developed self-adapting algorithms were then compared , with respect to convergence , with a traditional genetic one , which contained constant rates of mutation and crossover . The experiments were conducted on six benchmark functions including two unimodal functions , three multimodal with many local minima , and one multimodal with a few local minima .
This work deals with the optimization of the decoupling capacitors of a power delivery network by using a nature-inspired algorithm . In particular , the capacitance value and the location of three decoupling capacitors are optimized in order to obtain an input impedance below a specific mask , by using a nature-inspired algorithm , the genetic one , in combination with two electromagnetic solvers used to compute the objective function . An experimental board is designed and manufactured ; measurements are performed to validate the numerical results .
Autonomousrepairand refurbishment of reprogrammable logic devices usingGenetic Algorithms canimprove thefault tolerance ofremote mission-critical systems . Thegoalofincreasing availability byminimizing therepair timeisaddressed inthis paperusing a CGTprunedGenetic Algorithm .
For each module defining the path- predicate representation with an expression in disjunction normal form . This expression is then used to create a genetic template ( schema ) in a special genetic system model . The secondary aim of this work was to construct a prototype system designed and implemented to employ a specially designed Case Based Management and Retrieval Mechanism and also to provide maintenance of the case library .
A success ( risk ) measure of guaranteed availability is proposed . Using a genetic system model , the authors describe the measure and study the effects of the guaranteed level and the observation period on it . Furthermore , they introduce a numerical approach for continuous-time Markov chain models which allows component-level modeling , Coxian failure and repair distributions , time-dependent failure and repair rates and deferred repair and nondeferred repair strategies to be handled .
From the reviews about implementation HOLAP with Genetic Algorithm approach was expected being used as a basis research on HOLAP implementation for query optimization on distributed database with genetic algorithm approach . Keyword s : Query Optimization ; Distributed database ; HOLAP ; OLAP ; Genetetic algorithm .
The method can ensure the truthfulness and credibility of the weights of parameters and avoid the subjectivity caused by experts . Furthermore , the probability of falling into local minima is reduced , because genetic the algorithm is used to optimize the neural network system .
Abstract Computational cost poses a major obstacle to the design of indoor environments with the current optimal method and computational fluid dynamics ( CFD ) . A novel optimization method integrating a genetic algorithm ( GA ) , an artificial neural network ( ANN ) , multivariate regression analysis ( MRA ) , and a fuzzy logic controller ( FLC ) was proposed in this paper to optimize the indoor environment and energy consumption based on simulation results . Thermal comfort ( predicted mean vote ) was set as the restrictive design objective .
Genetic algorithm ( GA ) is one of the standard solutions to solve many optimization problems . One of a GA type used for solving a case is island model GA ( IMGA ) . Localization strategy is a brand-new feature for IMGA to better preserves its diversity .
The intrinsic strength of population based solution of GA offers asuperior advantage for this type of scheduling problems . Simulation results demonstrate the effectiveness of GA based scheduling scheme compared with the well-known M-LWDF scheduling rule and the simulated annealing method . It can offer better video quality at the receiver within the required delay bound .
The algorithm is suitable for blind spectrum sensing where the properties of the signal to be detected are unknown . The GA optimized SVD gives better result in the low signal to noise ( SNR ) environment .
The objective function is to minimize the amount of load shed while maximizing the lowest swing frequency at all stages . To validate the proposed GOA-based UFLS , a comparison with adaptive , particle swarm optimization ( PSO ) and genetic algorithm ( GA ) UFLS is carried out for different disturbances . Two different case study systems are considered to test the accuracy and reliability of the proposed algorithm : the IEEE 9-bus and 39-bus systems .
Abstract Genetic Algorithm ( GA ) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive . A review of the literature shows extensive research attempting to adapt and develop the standard GA .
Ten volunteers have participated of these studies , in which five participants were experienced designers and the remaining five were non-experienced designers of analog CMOS ICs . Our experimental results have shown that the iGA is capable of reducing the OCT in about 86 % in relation to the evolution process by using a non-interactive standard GA . More interestingly , both volunteers ' teams have obtained similar OCTs and OTAs ' robustness , indicating that iGA might be helpful not only to avoid the common optimization issue of local minima due to the human interaction with the computational tool ( iMTGSPICE ) , but also it is capable of producing similar results in terms of OCTs and OTA 's robustness .
In this way , the adapting for operators prevents its premature convergence and the adapting in the range of coverage causes maximum use of the problem ’s important resources and better performance of the algorithm in each step of its run . In the proposed GA ( GAIA ) , initially the adapting in the amount of algorithm ’s operators based on the solutions ’ tangent rate for premature convergence is done , then the adapting in the range of coverage of operators ’ algorithm , in first step , happens by operators convergence on BR ( which was detected initially ) and , in the next step , occurs by operators convergence on the elite solutions so that the search process focuses on more probable ranges than the whole range of solution . Comparing the problem results in the static state with the results of other available methods in the literature indicated high efficiency of the proposed method .
When GA is searching test data for a particular target branch heuristically , it may reach the other target branches , if so happen , then those test data will get stored into the buffer space hence it is not necessary to run GA to cover that branch . Thus the Buffered Genetic Algorithm ( BGA ) approach outperforms the other GA based automated test data generation approaches in terms of number of iterations and search effectiveness . The proposed approach employs control flow graph to traverse and predicate the branch coverage .
Unfortunately , these parameters interact with each other in a complicated way and determining which parameter set is best to use for a specific problem can be a complex task requiring much trial and error . This inherent drawback is overcome in this paper where it presents two adaptive GA approaches to HLS , the adaptive GA operator probability ( AGAOP ) and adaptive operator selection ( AOS ) and compares the performance to the standard GA ( SGA ) on eight digital logic benchmarks with varying complexity . The AGAOP and AOS are shown to be far more robust than the SGA , providing fast and reliable convergence across a broad range of parameter settings .
Softmax ( 5 ) Here , by CFS we denote a convolutional layer with F filters each of size S , by SP#S a spectral pooling layer with output dimensionality S , and GA the global averaging layer described in Lin et al . ( 2013 ) .
In this paper we study the problem of answering cloze - style questions over documents . Our model , the Gated - Attention ( GA ) Reader , integrates a multi - hop architecture with a novel attention mechanism , which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader . This enables the reader to build query - specific representations of tokens in the document for accurate answer selection .
They show state - of - the - art language modeling and parsing performance . We investigate what information they learn , from a linguistic perspective , through various ablations to the model and the data , and by augmenting the model with an attention mechanism ( GA - RNNG ) to enable closer inspection . We find that explicit modeling of composition is crucial for achieving the best performance .
Head rules , conversely , are analogous to giving all attention to one constituent , the one containing the lexical head . We now formally define the GA - RNNG ’s composition function . Recall that is the concatenation of the vector representations of the RNNG ’s data structures , used to assign probabilities to each of the actions available at timestep ( see Fig .
If the endocentric hypothesis is true ( that is , the representation of a phrase is built from within depending on its components but independent of explicit category labels ) , then the nonterminal types should be easily inferred given the endocentrically - composed representation , and that ablating the nonterminal information would not make much difference in performance . Specifically , we train a GA - RNNG on unlabeled trees ( only bracketings without nonterminal types ) , denoted U - GA - RNNG . This idea has been explored in research on methods for learning syntax with less complete annotation .
If endocentricity is largely sufficient to account for the behavior of phrases , where do our robust intuitions for syntactic category types come from ? We use t - SNE to visualize composed phrase vectors from the U - GA - RNNG model applied to the unseen test data . Fig .
Comparing with the AS Reader , ReasoNet shows the signi ca nt improvement by capturing multi - turn reasoning in the paragraph . Iterative Attention Reader , EpiReader and GA Reader are the three multi - turn reasoning models with xed reasoning steps . ReasoNet also outperforms all of them by integrating termination gate in the model which allows di erent reasoning steps for di erent test cases .
Consequently , the deployment optimization problem is abstracted into a combinatorial optimization problem . Then optimization model including the graph structure , cost function and heuristic function for the algorithm is built to meet the requirement of the heuristic search algorithm , and its correctness is also strictly proofed . Finally , experiment in real city area with occlusions ( such as building ) validates the usability of our method .
This paper improves the laplacian method and achieves the mid-result which contains whole road information . Based on image from ADS40,we tried the heuristic searching procedure to extract road target from the mid-result . And the experiment indicates that this method is effective and reaches the expected purpose .
It is the process of determining whether two systems are equivalent to each other according to some mathematically deflned notion of equivalence . In this paper to improve e‐ciency we present a procedure , based on heuristic searches , for checking the well-know simulation relation deflned by Milner . We use heuristic mechanisms for the exploration of the search space , in order to avoid the construction of the complete state graph .
To improve localization accuracy , we define an evaluation feedback metric to quantify the estimation errors and formulate jammer localization as a nonlinear optimization problem , whose optimal solution approaches jammer 's true position . We exploit a heuristic search based algorithm for approximating the global optimal solution , and our extensive simulation shows that our error-minimizing-based algorithm outperforms existing algorithms .
Our work applies to A*-based HTN planners with lifting . We prove that the planner finds the plan of maximal expected utility , while retaining its lifting capability and efficient heuristic-based search . We show how to learn the probabilities online , which allows a robot to adapt by replanning on execution failures .
With the tumultuous increase in acceptance of service oriented architecture , and thus , a rise in the number of web services , skilled software developers spend a lot of time composing web service workflows , rather than creating innovative and efficient services . Hence , we put forward a technique of code reusability that utilizes heuristic based search methods to automate service workflow composition by weighting quality of service criteria by relevance and importance to the users . We implement a novel and heuristic-based graph creation and search algorithm where the heuristic function value is calculated through the uniform cost search based on each of the quality of service criteria specified by the user .
Abstract Different schemes for the parallelization of a new algorithm to accelerate the Ray-Tracing for computing the electrical field radiated from or scattered by complex objects using high frequency techniques are presented . This algorithm is based on a combination of the Angular Z -Buffer ( AZB ) , the Space Volumetric Partitioning ( SVP ) and the A*Heuristic search method . The algorithm is very useful for different applications .
A combination of several ray-tracing acceleration techniques for computing very efficiently the radar cross section ( RCS ) of complex targets has been implemented in a new computer tool . The AZB and SVP algorithms together with the A * heuristic search method have been developed to consider multiple iterations between different flat surfaces that compound the target .
Traditional project scheduling and optimization methods have been unable to fully meet the rapid development of modern project management needs . The PSO ( Particle Swarm Optimization ) is a simulation of birds the heuristic search algorithm mechanisms , which function optimization , constrained optimization , minimax problems , such as multi-objective optimization problem . It has become an important branch of the many related optimization fields .
The main objective of the present work was to propose a small-hole EDM-drilling methodology using the heuristic search techniques to calculate the optimal toolpoint path that minimizes the small-hole EDM-drilling total machining time . Firstly , the tool points of the small-hole EDM-drilling are formulated as a traveling salesman problem ( TSP ) , and a new heuristic search technique algorithm of guided fast local search ( GFLS ) is proposed ; to find the optimal small-hole EDM-drilling toolpoint path . This optimal toolpoint path ; is the path that passes through all toolpoints and has the minimum machining time .
That is , the model is trained to minimize . Once the decoder is trained , discrete sequence generation can be performed by approximately maximizing the probability of the target sequence under the conditional distribution , , where we use the notation to emphasize that the decoding process requires heuristic search , since the RNN model is non - Markovian . In practice , a simple beam search procedure that explores prospective histories at each time - step has proven to be an effective decoding approach .
In recent years there has been much interest in the Monte Carlo tree search algorithm , a new , adaptive , randomized optimization algorithm . In fields as diverse as Artificial Intelligence , Operations Research , and High Energy Physics , research has established that Monte Carlo tree search can find good solutions without domain dependent heuristics .
Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy . We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree .
In this work , we present a MCTS-based Go-playing program which uses convolutional networks in all parts . Our method performs MCTS in batches , explores the Monte Carlo search tree using Thompson sampling and a convolutional network , and evaluates convnet-based rollouts on the GPU . We achieve strong win rates against open source Go programs and attain competitive results against state of the art convolutional net-based Go-playing programs .
Due to its simplicity of the rules and high degree of difficulty , Ms. Pac-Man has become an at- tractive subject of research on digital game AI . In Ms. Mac-Man , Monte Carlo Tree Search-based , especially UCT-based players tend to give better performance than rule-based systems . In this study , we propose the improvement of Monte-Carlo tree search using the evaluation function that have been learned in TD ( ) .
Then , we formulate the energy-efficient task assignment problem by taking into account the necessary constraints . We next propose a Monte Carlo Tree Search based algorithm , named , TA-MCTS for the task assignment problem . Simulation results show that compared to four alternative benchmark solutions in literature , our proposal can reduce energy consumption up to 45.37 percent .
We prove that standard no-regret algorithms can be used to learn optimal strategies for a scenario where the opponent uses one of these response functions . We demonstrate the effectiveness of this technique in Leduc Hold'em against opponents that use the UCT Monte Carlo tree search algorithm .
We prove that this technique can produce optimal strategies for playing against pretty-good responses . We empirically demonstrate the effectiveness of the technique by finding static strategies that are strong against Monte Carlo opponents who learn by sampling our strategy , including the UCT Monte Carlo tree search algorithm .
Presentation ID : PM_D32 Abstract : Not fully finalized , but I will examine how a Monte Carlo tree search algorith can be used in two particular finite games and the results .
Furthermore , the proposed solution was implemented as a parallel adaptive neural network for a graphics processing unit that achieves a performance compatible with the ones obtained for deep learning methods . HearthBot win rate was evaluated in two experiments playing against a Monte Carlo tree search heuristic with competitive decks on a HearthStone simulator called Metastone . Results show that the proposed solution allows HearthBot to obtain an average win rate performance of 80 % against known decks and 70 % against unknown decks .
In order to identify and select feasible container layouts that are practical and easy to load , a measure of disorder , based on the concept of entropy in physics and information theory , is derived . Finally , a novel method of directing a Monte-Carlo tree search process using the derived entropy measure is employed , to generate loading layouts that are comparable to those produced by expert human loaders . In summary , this thesis presents a new approach for dealing with real-world container loading in a warehouse environment , particularly in instances where layout complexity is of major importance ; such as the loading of heavy palletised goods using forklift trucks .
It enables us to customize each portion of the Monte Carlo Tree Search algorithm , as well as gives us the possibility to monitor our simulations with several data visualizers . The simulation results have shown that a Monte Carlo Tree Search simulation can be a very challenging opponent in a game of Texas Hold'em . Unfortunately , its decision-making speed can not be compared to other algorithms with more domain specific knowledge .
For most practical sequence generation tasks , e.g. machine translation , the reward signal is meaningful only for the entire sequence , for instance in the game of Go , the reward signal is only set at the end of the game . In those cases , state - action evaluation methods such as Monte Carlo ( tree ) search have been adopted . By contract , our proposed SeqGAN extends GANs with the RL - based generator to solve the sequence generation problem , where a reward signal is provided by the discriminator at the end of each episode via Monte Carlo approach , and the generator picks the action and learns the policy using estimated overall rewards .
Thus , to evaluate the action - value for an intermediate state , we apply Monte Carlo search with a roll - out policy to sample the unknown last tokens . We represent an - time Monte Carlo search as where and is sampled based on the roll - out policy and the current state . In our experiment , is set the same as the generator , but one can use a simplified version if the speed is the priority .
We also performed a set of experiments to investigate the robustness and stability of training SeqGAN . For future work , we plan to build Monte Carlo tree search and value network to improve action decision making for large scale data and in the case of longer - term planning . section : Acknowledgments
The corresponding representation learning issue has some parallels to the representation problem that we address by using an autoecoder , but it is unclear how the appropriate metric for the prior methods can be acquired automatically on tasks with raw sensory input , such as the Atari games in our experimental evaluation . Methods based on Monte - Carlo tree search can also scale gracefully to complex domains , and indeed previous work has applied such techniques to the task of playing Atari games from screen images . However , this approach is computationally very intensive , and requires access to a generative model of the system in order to perform the tree search , which is not always available in online reinforcement learning .
AlphaZero learns these move probabilities and value estimates entirely from self - play ; these are then used to guide its search . Instead of an alpha - beta search with domain - specific enhancements , AlphaZero uses a general - purpose Monte - Carlo tree search ( MCTS ) algorithm . Each search consists of a series of simulated games of self - play that traverse a tree from root to leaf .
The resolve of this research is planned as the first step of four-tier test-formatted HCCT development as one of investigative test instrument on Hydrostatic . The research method used the 4D model which has four comprehensive steps : 1 ) defining , 2 ) designing , 3 ) developing and 4 ) disseminating . The instrument developed has been tried to 30 students in one of senior high schools .
Through several experiments we show that A4 can bypass AdGraph about 60 % of the time , which surpasses the state-of-the-art attack by a significant margin of 84.3 % ; in addition , changes to the visual layout of the web page due to these perturbations are imperceptible . We envision the algorithmic framework proposed in A4 is also promising in improving adversarial attacks against other learning-based web applications with similar requirements .
We deduce the parameters that affect the DOF during the pickup process through theoretical analysis . In the modeling software C4D ( Cinema 4D ) the experiment is carried out . The results show that off-axis method is better , and verify the correctness of the calculation formula of DOF from the pickup process .
The transfer inefficiency increases sharply as feature size is reduced but decreases as oxide thickness is reduced . The fabrication and testing of C4D 's with barrier lengths in the range 1.75 µm to 5 µm are described , and the measured transfer inefficiencies compare well with theoretical predictions .
This R&D aims to develop e-modules based on blended learning in the cost esti-mating course . The research used the R&D method with the 4D ( four-D ) model carried out through four main stages , namely define , design , development , and dissemination . The e-module development was limited only to the assessment and limited dissemination to experts or the development stage .
The paper describes development of a shoulder joint support equipment for the athlete to learn proper movement of his shoulder in such sports and games as baseball and tennis . A simple 4DoF model of the scapula , which can change position relative to the human body , is proposed . The scapula is attached to the human body via four or more muscles .
In this paper , three sets of neural networks were trained respectively for three wavelengths:9,10 and 12 μm . Results of the experiment show that this fast calculation approach could calculate the top of atmosphere radiance with the error less than 0.1 K compared with 4 A , and its running speed is 100 times faster than that of 4 A for a single wavelength . This approach also enjoys more flexibility for the choosing of spectral channels .
Unit information for 4A4 : Continuum Mechanics ( MATH7424 ) , a unit offered by the University of Western Australia .
The application of multi-CCD splicing technology to a high resolution scanner is described . For the image scanning system developed with 4CCD technology , the designed physical resolution is 700 DPI , the resoluble interpotations are 350 DPI and 1400 DPI , the requirement of 0.08 m line width image is satisfied , the relative accuracy of image scanning is 0.1 mm , the horizontal splicing accuracy and vertical installation accuracy are controlled within a pixel , the corresponding object plane is 0.036 mm .
Hence , the 4D-IS concept with its ability to capture a full image cube at a single exposure and provide real time data processing offers a new and enhanced capability over present sensors . The 4D-IS uses a reformatter fiber optics to map a 2D image to a linear array that serves as an input slit to an imaging spectrometer . The paper describes three such instruments , a VNIR , a MWIR , and a dual band MW/LWIR .
This CONOPS utilises intermeshed Hosts ; entities and architectural design of the aerial corridor using ‘ 4 dimensional Security containment box ’ ( 4D – SCB ) . That is , a 4D – SCB ( axis X , Y , Z and time ) that requires a micro Radar network linked to AFCS — NCC .
ACTUALITY AND DEVELOPMENTAL TREND FOR GENETIC ALGORITHMS FEATURE TRANSFORMATION: A GENETIC‐BASED FEATURE CONSTRUCTION METHOD FOR DATA SUMMARIZATION Fast Genetic Algorithm for Pick-up Path Optimization in the Large Warehouse System Adapting Crossover and Mutation Rates in Genetic Algorithms Genetic Algorithm by using MATLAB Program Investigation of Genetic Algorithms with Self-Adaptive Crossover, Mutation, and Selection Decoupling Capacitors Placement at Board Level Adopting a Nature-Inspired Algorithm Expediting GA-BasedEvolution UsingGroupTesting Techniques for Reconfigurable Hardware1 THE GENERATION OF TEST DATA USING GENETIC ALGORITHMS AND CASE BASED REASONING FOR SAFETY CRITICAL SYSTEMS A Measure of Guaranteed Availability and its Numerical Evaluation Review: Implementasi Holap Untuk Optimasi Query Sistem Basis Data Terdistribusi Dengan Pendekatan Algoritma Genetik Application of a neural network system combined with genetic algorithm to rank coalbed methane reservoirs in the order of exploitation priority Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller Performance analysis of localization strategy for island model genetic algorithm Enhancing quality of video services over wireless networks GA optimized SVD based signal detector for Cognitive radio Networks A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach Fluid Genetic Algorithm (FGA) Automatic Optimization of Robust Analog CMOS ICs: An Interactive Genetic Algorithm Driven by Human Knowledge Solving the Dynamic Job Shop Scheduling Problem using Bottleneck and Intelligent Agents based on Genetic Algorithm A Buffered Genetic Algorithm for Automated Branch Coverage in Software Testing Adaptive GA: An essential ingredient in high-level synthesis Spectral Representations for Convolutional Neural Networks Gated-Attention Readers for Text Comprehension What Do Recurrent Neural Network Grammars Learn About Syntax? ReasoNet: Learning to Stop Reading in Machine Comprehension Deployment optimization for camera network coupling with geographic environment An Improved Road Extraction Method Based on Laplacian and Heuristic Searching Procedures Heuristic for Simulation Checking Error minimizing jammer localization through smart estimation of ambient noise Integration of Online Learning into HTN Planning for Robotic Tasks Automated Composition of Web Service Workflow - A Novel QoS-Enabled Multi-criteria Cost Search Algorithm A study of the efficiency of the parallelization of a high frequency electromagnetic approach for the computation of radiation and scattering considering multiple bounces Iterative method for computing N-reflections between flat surfaces in the rcs prediction of complex targets The Project Research for Optimal Scheduling Based on Intelligent Algorithm A Proposed Algorithm for Optimizing the Toolpoint Path of the Small-Hole EDM-Drilling Sequence-to-Sequence Learning as Beam-Search Optimization A New Method for Parallel Monte Carlo Tree Search Tree Search Techniques for Minimizing Detectability and Maximizing Visibility Convolutional Monte Carlo Rollouts in Go Improvement of Monte Carlo Tree Search AI using TD(λ) in Ms. Pac-Man A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing Using Response Functions to Measure Strategy Strength Using Response Functions for Strategy Training and Evaluation Application of Monte Carlo Tree Search in Two Player Finite Games HearthBot: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone Applying computational intelligence to a real-world container loading problem in a warehouse environment Monte Carlo Tree Search in Poker Games SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Promoting the hydrostatic conceptual change test (HCCT) with four-tier diagnostic test item A4 : Evading Learning-based Adblockers Effects of pickup mode and parameters of integral imaging based on C4D Low frequency transfer efficiency of E-beam fabricated conductively connected charge-coupled device E-Module of Cost Estimating Course in Building Construction Vocational Undergraduate Program Faculty of Engineering Universitas Negeri Jakarta Development of a shoulder joint support equipment based on scapula motion analysis: A three dimensional scapula motion model Fast calculation approach for the hyperspectral infrared radiative transfer model based on artificial neural network UWA Handbook, Unit Information for 4A4: Continuum Mechanics (MATH7424) DESIGN OF HIGH RESOLUTION IMAGE SCANNER WITH CCD SPLICING TECHNOLOGY Development of four-dimensional imaging spectrometers (4D-IS) System engineering and logistical UAS corridors : Challenge the present to drive the future
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ICSI (3) HAIS (1) SNPD SBCCI IEEE Congress on Evolutionary Computation NIPS ACL EACL CoCo@NIPS MASS AAAI Spring Symposium: Designing Intelligent Robots ENASE ICEE EMNLP AAAI AAAI ArXiv ArXiv ICARA
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