Forced outages of system components are taken into account as the system uncertainties . A hybrid method as the combination of gravitational search algorithm ( GSA ) and primal-dual interior point ( PDIP ) method is employed to solve the nonlinear programming ( NLP ) problem of the NEP . In the proposed hybrid method , the operation sub-problems are solved by the PDIP method under the worst-case single component contingencies , while the expansion plan is defined as scenario independent variables obtained by the GSA algorithm . In this paper , Thyristor Controlled Series Capacitor ( TCSC ) is selected to enhance the loadability in the transmission line . To improve the maximum power transfer capability by this device an improved gravitational search algorithm ( IGSA ) is suggested . Velocity of each agent is improved by this proposed algorithm . The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance . The obtained results are compared with the Grasshopper Optimization Algorithm ( GOA ) , and the hybrid Particle Swarm Optimization ( PSO ) and Gravitational Search Algorithm ( PSOGSA ) . The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs ’ and SCBs ’ locations and capacities simultaneously . These techniques are the subject of the emerging field of Knowledge Discovery in Databases ( KDD ) . In this paper , an algorithm to mine the intelligence rules from the input data using gravitational search ( GS ) algorithm is proposed . Initially , the significant patterns are selected from the input database with the use of measures like density ratio , sequence index , and density index . Specifically , advanced detection approaches created by combining or integrating evolutionary algorithms and neural networks have shown better detection performance than general machine learning approaches . The present study reports two new hybrid intrusion detection methods ; one is based on gravitational search ( GS ) , and other one is a combination of GS and particle swarm optimization ( GSPSO ) . These two techniques have been successfully implemented to train artificial neural network ( ANN ) and the resulting models : GS-ANN and GSPSO-ANN are successfully applied for intrusion detection process . This paper proposed an improved optimization algorithm for engineering optimization problems . This improved optimization algorithm , namely , gravitational search strategy-assisted rider optimization algorithm ( GSSROA ) , combines the gravitational search strategy ( GSS ) with the rider optimization algorithm ( ROA ) . In the GSSROA , the search step can be adaptively adjusted in each iteration . In this paper , a novel Gravitational Artificial Bee Colony ( GABC ) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems . In this approach , the gravitational search strategy was introduced into the artificial bee colony algorithm , and a gravitational bee colony was established . The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation , which makes the optimization process more effectively and efficiently . To improve the exploration and exploitation abilities of the standard BFO , this paper proposes an effective bacterial foraging optimization ( EBFO ) . First a gravitational search strategy is incorporated into the chemotaxis step to adjust its unit length according to the swarm information . Then , a swarm diversity strategy is integrated into the reproduction step to enhance the reproduction mode depending on the swarm diversity . Gravitational search algorithms have been applied to the data mining tasks like classification , clustering objects and mining association rules etc . This paper presents a survey on the applications of Gravitational Search based algorithms in data mining and indicates the scope of further research on using GSAs in the field of data mining . The state monitoring issue of the induced draft fan in a thermal power plant by employing the gravitational searching algorithm optimized BP neural network is investigated in this paper . A new method to estimate the air quantity of the induced draft air of a thermal power plant is proposed based on the historical operation data extracted from the supervisory information system ( SIS ) . In order to predict the air quantity of the induced draft fan , all the variables infecting the air quantity are selected as the inputs of the BP neural network , which is optimized by the gravitational searching algorithm . After training the neural network , a prediction model of the air quantity is established and the maximum prediction error is less than 3 % . Then , the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance . The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error . The dc power transfer distribution factors are utilized to compute ATC in bilateral transactions environment . The gravitational search assisted algorithm is employed to manage congestion in a pool electricity market . Its performance is compared with OPF based on other heuristics . Multi-objective optimization method based on analytic hierarchy technique is employed to solve the problem . The Pareto-optimal set is extracted with gravitational search style and the best solution is fund by AHT manner . Studies are carried out on the modified 30-bus and 24-bus IEEE test system to confirm the capability of the presented model . We consider several benchmark problems including two and three-objective problems . We approximate Pareto-set of each problem using a simple version of MOEA/D algorithm , and then the pruning algorithm is applied . The experimental result has shown that our pruning algorithm provides a robust sub-set of Pareto-optimal solutions for each benchmark problem . The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm ( MOEA/D ) . While each of the two MOEA/D methods works well on different classes of problems , the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper . This paper presents results on unconstrained problems , and the sequel paper considers constrained and other specialties in handling many-objective optimization problems . Moreover , a simple yet effective method called Pareto adaptive scalarizing ( PaS ) approximation is proposed to approximate the optimal p value . In order to demonstrate the effectiveness of PaS , we incorporate PaS into a state-of-the-art decomposition-based algorithm , i.e. , multiobjective evolutionary algorithm based on decomposition ( MOEA/D ) , and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives . Experimental results demonstrate that the PaS is effective . To enhance the spread of Pareto optimal solutions in the objective space and improve the search performance of MOEA/D especially in many-objective optimization problems , in this work we propose the inverted PBI scalarizing approach which is an extension of the conventional PBI . We use many-objective knapsack problems and WFG4 problems with 2 - 8 objectives , and compare the search performance of NSGA-III and four MOEA/Ds using the weighted Tchebycheff , the weighted sum , the PBI and the inverted PBI . As results , we show that MOEA/D using the inverted PBI achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to obtain a widely spread Pareto front in the objective space . To address the above problem , this paper proposes an evolutionary search method with multiple utopian reference points in MOEA/Ds . Similar to the existing MOEA/Ds , each solution is associated with one weight vector , which provides an evolutionary search direction , while the novelty of our approach is to use multiple utopian reference points , which can provide evolutionary search directions for different weight vectors . Corner solutions are used to approximate the nadir point and then multiple utopian reference points for evolutionary search can be constructed based on the ideal point and the nadir point , which are uniformly distributed on the coordinate axis or planes . This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms ( MOEA/Ds ) to reduce the unnecessary usage of computational resource . In MOEA/D , a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach . Moreover , this study develops distinct models to satisfy different needs of the DM , thus providing a new way to deal with preference-based multiobjective optimization . Additionally , in terms of the shortcoming of MOEA/D-PRE , an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems . Although there exist numerous literatures on facility layout in manufacturing systems , studies on department layout in hospitals are relatively scarce . In this paper , we proposed a MOEA/D based approach for a hospital department layout problem , where three objectives are simultaneously optimized , namely patient flow cost , the closeness of departments and rearrangement cost . A constraint handling technology is introduced to deal with infeasible solutions . Besides , the critical path based carbon saving method is also used . Computational comparisons demonstrate the effectiveness of the operator-selection strategy and the superior quality of the MOEA/D-M to other algorithms . Our rigorous theoretical analysis shows that by properly setting the number of subproblems , the upper bounds of expected running time of MOEA/D-C obtaining a set of solutions to cover the Pareto fronts ( PFs ) of these problems are apparently lower than those of the one with mutation-only ( MOEA/D-M ) . Moreover , to effectively obtain a set of solutions to cover the PFs of these problem , MOEA/D-C only needs to decompose these MOPs into several subproblems with a set of simple weight vectors while MOEA/D-M needs to find Ω(n ) optimally decomposed weight vectors . This result suggests that the use of crossover in decomposition-based MOEA can simplify the setting of weight vectors for different problems and make the algorithm more efficient . Comparison of EMOABC with MOABC and the standard ABC ( SABC ) shows better performance of EMOABC . Furthermore , a comparative analysis is also done with non-dominated sorting genetic algorithm II ( NSGA-II ) , Pareto-dominance particle swarm optimization ( PDPSO ) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms , namely , MOEA/D-1 and MOEA/D-2 . Results indicate that EMOABC demonstrated superior performance than all the other algorithms . In this paper , we propose an improved ideal point setting method in the framework of MOEA/D. MOEA/D decomposes a multi-objective optimisation problem into a number of scalar optimisation problems and optimise them simultaneously . The performance of MOEA/D is highly relate to its decomposition method , and the proposed ideal point setting approach is used in the weighted Tchebycheff ( TCH ) and penalty-based boundary intersection ( PBI ) decomposition approach . In bioinformatics , inference of genetic networks is one of the most important problems , in which mutual interactions among genes are estimated by using gene-expression time-course data . Network-Structure-Search Evolutionary Algorithm ( NSS-EA ) is a promising inference method of genetic networks that employs S-system as a model of genetic network and a genetic algorithm ( GA ) as a search engine . In this paper , we propose an implementation of NSS-EA running on a multi-PC-cluster grid computing environment where multiple PC clusters are connected over the Internet . One effective way to reduce the number of erroneous regulations is to apply inference methods that use a priori knowledge on the properties of the genetic networks . The existing inference methods adopting this approach generally use a priori knowledge and the observed gene expression data simultaneously to determine whether or not the target genetic network actually contains each of the candidate regulations . In this study , we establish a new framework for “ using a priori knowledge after genetic network inference . In this thesis , we combine the idea of regulatory variation in a modular developmental system with Kauffmans hypothesis of cell types as attractors . We present a simple model for the ontogenesis of two-dimensional shapes an attractor network of regulatory genes controls the deployment of genetic network modules that explicitly determine growth and phenomenological attributes . We use this model to show that : 1 . This paper is dedicated to extend EDA to solve one of the most active research problems - stock trading , which has been rarely revealed in the EDA literature . A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming ( RPMBGNP ) is investigated to create stock trading rules . With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling , we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data , where much higher profits are obtained than traditional non-EDA models . In this study we provide a detailed technical description of the use of grammatical evolution to optimize neural networks ( GENN ) for use in genetic association studies . We compare the performance of GENN to that of a previous machine‐learning NN application — genetic programming neural networks in both simulated and real data . We show that GENN greatly outperforms genetic programming neural networks in data sets with a large number of single nucleotide polymorphisms . Indeed , logic , classifiers and machine learning methods , probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various approaches that artificial intelligence has offered in dealing with real life problems . This paper attempts to explore the research that has been conducted on the application of the most typical and popular soft computing techniques [ fuzzy logic , neural networks , Bayesian networks , genetic programming and hybrid approaches such as neuro-fuzzy systems and genetic programming neural networks ( GPNNs ) ] in student modelling over the decade 1999 - 2009 . This latest research trend is a part of every intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student 's answers . 11.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation . 11.4 Genetic programming neural networks for determining gene-gene interactions in epidemiology . 11.5 Application guidelines . In this paper , we propose a genetic network programming based system to mine equalized association rules in multi concept layers of ontology . There are three modules in this system : database , equalizer and GNP miner . A method of association rule mining using Alternate Genetic Network Programming ( aGNP ) is proposed . GNP is one of the evolutionary optimization techniques , which uses directed graph structures as genes . A Genetic Network Programming Architecture ( GNP ) is applied to a multi- agent problem where cooperation of agents are required . GNP is a kind of evolutionary methods inspired from Genetic Programming(GP ) . Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data . In particular , two approaches , Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm . Good preliminary results were obtained with 100 % training and 100 % testing accuracy that differentiate normal versus malignant samples . We use genetic programming evolved networks , vector fields , and signal processing to study time varying-exposures where trust is implied ( e.g. a conversion event from attention flow to financial commitment ) . The datasets are behavioral finance time series ( from on-chain data , such as fees , and off-chain data , such as clickstreams ) , which we use to elaborate on various complexity metrics of causality , through the creation parametric network graphs . Therefore , if the parameters are adjusted by an appropriate way , the performance of the force generation method will be improved . In this paper , real-coded genetic algorithm ( RCGA ) , which is an optimization technique , is applied to the parameter optimization of the VDP oscillator because it is easily implementable for nonlinear systems . Simulation results show that RCGA succeeded in finding out sets of better parameters than ones used in the previous study . This paper presents a Real Coded Genetic Algorithm ( RCGA ) for finding the load flow solution of electrical power systems . The proposed method is based on the minimization of the real and reactive power mismatches at various buses . The test results of four benchmark functions verify the advantages of the algorithm in global search , convergence speed and avoiding prematurity . In comparison of two case studies with the real genetic algorithm ( RGA ) , the evolution programming ( EP ) , the particle swarm optimization ( PSO ) , the ant colony optimization ( ACO ) , and the standard glowworm swarm optimization ( GSO ) , the MAGSO provides significantly robust solutions of ELD problem of a large-scale hydropower station . The proposed scheme enhances the grey prediction method of difference equations , which is a single variable second order grey model ( DGM(2,1 ) model ) . It is performed by a real-valued genetic algorithm ( RGA ) with a system performance index as fitness function . We apply the integral of time multiplied by the square error criterion ( ITSE ) to form a suitable fitness function in RGA . In array pattern synthesis , it is often designed to achieve the desired radiation pattern . In this paper , real-coded genetic algorithm ( RGA ) optimization method is presented to optimize the value of weights of each antenna element to minimize side lobe level of the uniform spaced linear array geometries with a certain main beam width . The optimization program is done by using MATLAB . In terms of the characteristics of water resource compound system and the conditions of water resources , an evaluation system of sustainable water resource utilization has been established according to the principles of sustainability . Projection pursuit model(PPM ) and real coding based genetic algorithm(RGA ) have been used in the evaluation , which can synthesize the multiple factors into a single pursuit value and attain an optimal scheme . The result shows that it is effective to solve the weighted human interference , and fulfill the evaluation of sustainable water resource utilization by an optimized sequence . With a kind of improved genetic algorithm(GA)real coding based accelerating genetic algorithm ( RAGA ) , one area 's hydrogeologic parameters have been inverse computed and set up a mathematic model that is coupled with numerical simulation . At the same time , it has been compared with application of real coding genetic algorithm(RGA ) and manual adjust parameters in inverse problem of hydrogeologic parameters . The study shows that RAGA has characters of global optimization and accelerative convergence when compared with the other two ways. Abstract This paper establishes the utility of a real number genetic algorithm ( RGA ) for direct learning neural control . The controller has a hierazchical structure where the leazning algorithm , RGA , is the higher level and a feedback neural controller is the lower . Various numerical optimization procedures are then realized to increase the engine power output and simultaneously avoid conditions leading to undesired self-ignitions . These are either based on the use of a non-evolutionary algorithm or employ a genetic algorithm in the case multiple contrasting objectives are set . The response surface methodology is also explored as a way to reduce the computational effort . In this paper , a novel hybrid approach is proposed for simultaneous feature selection and feature weighting with k value selection of K-NN rule based on Biogeography based optimization ( BBO ) . The 6 evolutionary algorithms and 14 non-evolutionary algorithms are used to compare and evaluate with the novel proposed algorithm ( BBO-KNN ) . The experimental results signify that the BBO-KNN has higher efficiency compared to other methods and is led to higher classification rate as well as effective data dimension reduction . Symbolic regression is a common application for genetic programming ( GP ) . This paper presents a new non-evolutionary technique for symbolic regression that , compared to competent GP approaches on real-world problems , is orders of magnitude faster ( taking just seconds ) , returns simpler models , has comparable or better prediction on unseen data , and converges reliably and deterministically . I dub the approach FFX , for Fast Function Extraction . Monte-Carlo Tree Search is a popular method to implement computer programs for board games , and its performance can be significantly improved by including static knowledge about the game , for example in the formof patterns learned from game records . Finding the right pattern shapes is still an open problem , and we propose in this paper an evolutionary-like method to optimize the pattern shapes . We avoid direct optimization through the heavy Monte-Carlo framework by using instead the performance of a machine-learning algorithm as an early indicator of the quality of the pattern shapes . Streaming media data access has been a problem for several years , and the problem becomes tougher in the mobile environment in which mobile users use mobile devices that are of rather limited storage space , preventing the clients from having a large cache . In this paper , we design a novel evolutionary caching algorithm for base stations to adapt to the user requests , so as to make the scheme more adaptive to the changing environment while maintaining good Byte Hit Ratio ( BHR ) or Number Hit Ratio ( NHR ) for the real world requests . We evaluate the effectiveness of our evolutionary caching algorithm through simulation studies , the results of which demonstrate that our scheme can obtain good performance on buffering streaming media data for user requests as far as the BHR and NHR metrics are concerned . Abstract The theory of order statistics is utilized in this paper to analyze the convergence velocity ϕ of simplified evolutionary algorithms based on mutation and ( μ + , λ)-selection . A general , representation-independent way of theoretical analysis is outlined and put in concrete terms for two specific objective functions : The binary counting ones objective function and the continuous sphere model . The proposed multi objective evolutionary method has been applied to the standard IEEE 30 bus six generators , fourteen generators and 40 thermal generating units , respectively , as small , medium and large test power system . The numerical results obtained with the proposed method based on tables and figures compared with other evolutionary algorithm of scientific literatures . The results regards that the proposed CIABC algorithm surpasses the other available methods in terms of computational efficiency and solution quality . Abstract Evolutionary games usually take into consideration individuals ’ strategies as the transformative characteristic which leads to the evolution of the population . Here , besides the strategies , interaction aspects are also considered as evolutionary attributes which can change over time as the replacement dynamic renovates the population choosing locally better individuals to reproduce . According to the industrial experts the amount of false positives and false negatives was too high for CouplingViewer to be industrially applicable . Analysis for root causes in this case study confirmed some known and identified some new research directions to improve the performance of evolutionary couplings analysis . No explicit conditions are imposed on survival : selection criteria are an emergent property of the global system and can not be deduced from local CA rules . As a result , populations undergo self-organized evolutionary processes leading to complex and unpredictable spatial and temporal dynamics on multiple scales . The study of these complex dynamics forms the primary focus of this thesis . In the study of innovation theory , some scholars believe that innovation process is the process of knowledge evolution . Cluster innovation is a complex economic activity based on knowledge resource , It is systematic , procedure , and with the evolutionary process of innovation self-organized . In this process , the knowledge structures of cluster innovation alternate by the path of " stability-bifurcation-stability " , and it also contains the gradual improvement of the structure of innovation knowledge linked to learning and the breakthrough innovation of such structure associated with variation . This search within a bounded space can be compared to the purposeful , constrained decision making process involved in designing . The generated solutions use the evolutionary concept of genetic algorithms to deduce solutions within the predefined design solution search space . Simple evolutionary concepts such as reproduction , crossover and mutation aid this search process . The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document , handling rich features of text data and dealing with the well known problem of the curse of dimensionality . In this thesis , we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods – Query Sense Clustering ( QSC ) , Dirichlet Weighted K-means ( DWKM ) , Multi-View Multi-Objective Evolutionary Algorithm ( MMOEA ) , Multi-objective Document Clustering ( MDC ) and Multi-Objective MultiView Ensemble Clustering ( MOMVEC ) . These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods .
Forced outages of system components are taken into account as the system uncertainties . A hybrid method as the combination of gravitational search algorithm ( GSA ) and primal-dual interior point ( PDIP ) method is employed to solve the nonlinear programming ( NLP ) problem of the NEP . In the proposed hybrid method , the operation sub-problems are solved by the PDIP method under the worst-case single component contingencies , while the expansion plan is defined as scenario independent variables obtained by the GSA algorithm .
In this paper , Thyristor Controlled Series Capacitor ( TCSC ) is selected to enhance the loadability in the transmission line . To improve the maximum power transfer capability by this device an improved gravitational search algorithm ( IGSA ) is suggested . Velocity of each agent is improved by this proposed algorithm .
The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance . The obtained results are compared with the Grasshopper Optimization Algorithm ( GOA ) , and the hybrid Particle Swarm Optimization ( PSO ) and Gravitational Search Algorithm ( PSOGSA ) . The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs ’ and SCBs ’ locations and capacities simultaneously .
These techniques are the subject of the emerging field of Knowledge Discovery in Databases ( KDD ) . In this paper , an algorithm to mine the intelligence rules from the input data using gravitational search ( GS ) algorithm is proposed . Initially , the significant patterns are selected from the input database with the use of measures like density ratio , sequence index , and density index .
Specifically , advanced detection approaches created by combining or integrating evolutionary algorithms and neural networks have shown better detection performance than general machine learning approaches . The present study reports two new hybrid intrusion detection methods ; one is based on gravitational search ( GS ) , and other one is a combination of GS and particle swarm optimization ( GSPSO ) . These two techniques have been successfully implemented to train artificial neural network ( ANN ) and the resulting models : GS-ANN and GSPSO-ANN are successfully applied for intrusion detection process .
This paper proposed an improved optimization algorithm for engineering optimization problems . This improved optimization algorithm , namely , gravitational search strategy-assisted rider optimization algorithm ( GSSROA ) , combines the gravitational search strategy ( GSS ) with the rider optimization algorithm ( ROA ) . In the GSSROA , the search step can be adaptively adjusted in each iteration .
In this paper , a novel Gravitational Artificial Bee Colony ( GABC ) optimization algorithm was proposed and utilized to the non-supervised pattern recognition problems . In this approach , the gravitational search strategy was introduced into the artificial bee colony algorithm , and a gravitational bee colony was established . The gravitational bee could search the global optimal result under the influence of both gravitational force and colony cooperation , which makes the optimization process more effectively and efficiently .
To improve the exploration and exploitation abilities of the standard BFO , this paper proposes an effective bacterial foraging optimization ( EBFO ) . First a gravitational search strategy is incorporated into the chemotaxis step to adjust its unit length according to the swarm information . Then , a swarm diversity strategy is integrated into the reproduction step to enhance the reproduction mode depending on the swarm diversity .
Gravitational search algorithms have been applied to the data mining tasks like classification , clustering objects and mining association rules etc . This paper presents a survey on the applications of Gravitational Search based algorithms in data mining and indicates the scope of further research on using GSAs in the field of data mining .
The state monitoring issue of the induced draft fan in a thermal power plant by employing the gravitational searching algorithm optimized BP neural network is investigated in this paper . A new method to estimate the air quantity of the induced draft air of a thermal power plant is proposed based on the historical operation data extracted from the supervisory information system ( SIS ) . In order to predict the air quantity of the induced draft fan , all the variables infecting the air quantity are selected as the inputs of the BP neural network , which is optimized by the gravitational searching algorithm . After training the neural network , a prediction model of the air quantity is established and the maximum prediction error is less than 3 % .
Then , the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance . The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error .
The dc power transfer distribution factors are utilized to compute ATC in bilateral transactions environment . The gravitational search assisted algorithm is employed to manage congestion in a pool electricity market . Its performance is compared with OPF based on other heuristics .
Multi-objective optimization method based on analytic hierarchy technique is employed to solve the problem . The Pareto-optimal set is extracted with gravitational search style and the best solution is fund by AHT manner . Studies are carried out on the modified 30-bus and 24-bus IEEE test system to confirm the capability of the presented model .
We consider several benchmark problems including two and three-objective problems . We approximate Pareto-set of each problem using a simple version of MOEA/D algorithm , and then the pruning algorithm is applied . The experimental result has shown that our pruning algorithm provides a robust sub-set of Pareto-optimal solutions for each benchmark problem .
The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm ( MOEA/D ) . While each of the two MOEA/D methods works well on different classes of problems , the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper . This paper presents results on unconstrained problems , and the sequel paper considers constrained and other specialties in handling many-objective optimization problems .
Moreover , a simple yet effective method called Pareto adaptive scalarizing ( PaS ) approximation is proposed to approximate the optimal p value . In order to demonstrate the effectiveness of PaS , we incorporate PaS into a state-of-the-art decomposition-based algorithm , i.e. , multiobjective evolutionary algorithm based on decomposition ( MOEA/D ) , and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives . Experimental results demonstrate that the PaS is effective .
To enhance the spread of Pareto optimal solutions in the objective space and improve the search performance of MOEA/D especially in many-objective optimization problems , in this work we propose the inverted PBI scalarizing approach which is an extension of the conventional PBI . We use many-objective knapsack problems and WFG4 problems with 2 - 8 objectives , and compare the search performance of NSGA-III and four MOEA/Ds using the weighted Tchebycheff , the weighted sum , the PBI and the inverted PBI . As results , we show that MOEA/D using the inverted PBI achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to obtain a widely spread Pareto front in the objective space .
To address the above problem , this paper proposes an evolutionary search method with multiple utopian reference points in MOEA/Ds . Similar to the existing MOEA/Ds , each solution is associated with one weight vector , which provides an evolutionary search direction , while the novelty of our approach is to use multiple utopian reference points , which can provide evolutionary search directions for different weight vectors . Corner solutions are used to approximate the nadir point and then multiple utopian reference points for evolutionary search can be constructed based on the ideal point and the nadir point , which are uniformly distributed on the coordinate axis or planes .
This paper proposes a new stopping criterion for decomposition-based multi-objective evolutionary algorithms ( MOEA/Ds ) to reduce the unnecessary usage of computational resource . In MOEA/D , a multi-objective problem is decomposed into a number of single-objective subproblems using a Tchebycheff decomposition approach .
Moreover , this study develops distinct models to satisfy different needs of the DM , thus providing a new way to deal with preference-based multiobjective optimization . Additionally , in terms of the shortcoming of MOEA/D-PRE , an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems .
Although there exist numerous literatures on facility layout in manufacturing systems , studies on department layout in hospitals are relatively scarce . In this paper , we proposed a MOEA/D based approach for a hospital department layout problem , where three objectives are simultaneously optimized , namely patient flow cost , the closeness of departments and rearrangement cost . A constraint handling technology is introduced to deal with infeasible solutions .
Besides , the critical path based carbon saving method is also used . Computational comparisons demonstrate the effectiveness of the operator-selection strategy and the superior quality of the MOEA/D-M to other algorithms .
Our rigorous theoretical analysis shows that by properly setting the number of subproblems , the upper bounds of expected running time of MOEA/D-C obtaining a set of solutions to cover the Pareto fronts ( PFs ) of these problems are apparently lower than those of the one with mutation-only ( MOEA/D-M ) . Moreover , to effectively obtain a set of solutions to cover the PFs of these problem , MOEA/D-C only needs to decompose these MOPs into several subproblems with a set of simple weight vectors while MOEA/D-M needs to find Ω(n ) optimally decomposed weight vectors . This result suggests that the use of crossover in decomposition-based MOEA can simplify the setting of weight vectors for different problems and make the algorithm more efficient .
Comparison of EMOABC with MOABC and the standard ABC ( SABC ) shows better performance of EMOABC . Furthermore , a comparative analysis is also done with non-dominated sorting genetic algorithm II ( NSGA-II ) , Pareto-dominance particle swarm optimization ( PDPSO ) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms , namely , MOEA/D-1 and MOEA/D-2 . Results indicate that EMOABC demonstrated superior performance than all the other algorithms .
In this paper , we propose an improved ideal point setting method in the framework of MOEA/D. MOEA/D decomposes a multi-objective optimisation problem into a number of scalar optimisation problems and optimise them simultaneously . The performance of MOEA/D is highly relate to its decomposition method , and the proposed ideal point setting approach is used in the weighted Tchebycheff ( TCH ) and penalty-based boundary intersection ( PBI ) decomposition approach .
In bioinformatics , inference of genetic networks is one of the most important problems , in which mutual interactions among genes are estimated by using gene-expression time-course data . Network-Structure-Search Evolutionary Algorithm ( NSS-EA ) is a promising inference method of genetic networks that employs S-system as a model of genetic network and a genetic algorithm ( GA ) as a search engine . In this paper , we propose an implementation of NSS-EA running on a multi-PC-cluster grid computing environment where multiple PC clusters are connected over the Internet .
One effective way to reduce the number of erroneous regulations is to apply inference methods that use a priori knowledge on the properties of the genetic networks . The existing inference methods adopting this approach generally use a priori knowledge and the observed gene expression data simultaneously to determine whether or not the target genetic network actually contains each of the candidate regulations . In this study , we establish a new framework for “ using a priori knowledge after genetic network inference .
In this thesis , we combine the idea of regulatory variation in a modular developmental system with Kauffmans hypothesis of cell types as attractors . We present a simple model for the ontogenesis of two-dimensional shapes an attractor network of regulatory genes controls the deployment of genetic network modules that explicitly determine growth and phenomenological attributes . We use this model to show that : 1 .
This paper is dedicated to extend EDA to solve one of the most active research problems - stock trading , which has been rarely revealed in the EDA literature . A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming ( RPMBGNP ) is investigated to create stock trading rules . With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling , we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data , where much higher profits are obtained than traditional non-EDA models .
In this study we provide a detailed technical description of the use of grammatical evolution to optimize neural networks ( GENN ) for use in genetic association studies . We compare the performance of GENN to that of a previous machine‐learning NN application — genetic programming neural networks in both simulated and real data . We show that GENN greatly outperforms genetic programming neural networks in data sets with a large number of single nucleotide polymorphisms .
Indeed , logic , classifiers and machine learning methods , probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various approaches that artificial intelligence has offered in dealing with real life problems . This paper attempts to explore the research that has been conducted on the application of the most typical and popular soft computing techniques [ fuzzy logic , neural networks , Bayesian networks , genetic programming and hybrid approaches such as neuro-fuzzy systems and genetic programming neural networks ( GPNNs ) ] in student modelling over the decade 1999 - 2009 . This latest research trend is a part of every intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student 's answers .
11.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation . 11.4 Genetic programming neural networks for determining gene-gene interactions in epidemiology . 11.5 Application guidelines .
In this paper , we propose a genetic network programming based system to mine equalized association rules in multi concept layers of ontology . There are three modules in this system : database , equalizer and GNP miner .
A method of association rule mining using Alternate Genetic Network Programming ( aGNP ) is proposed . GNP is one of the evolutionary optimization techniques , which uses directed graph structures as genes .
A Genetic Network Programming Architecture ( GNP ) is applied to a multi- agent problem where cooperation of agents are required . GNP is a kind of evolutionary methods inspired from Genetic Programming(GP ) .
Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data . In particular , two approaches , Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm . Good preliminary results were obtained with 100 % training and 100 % testing accuracy that differentiate normal versus malignant samples .
We use genetic programming evolved networks , vector fields , and signal processing to study time varying-exposures where trust is implied ( e.g. a conversion event from attention flow to financial commitment ) . The datasets are behavioral finance time series ( from on-chain data , such as fees , and off-chain data , such as clickstreams ) , which we use to elaborate on various complexity metrics of causality , through the creation parametric network graphs .
Therefore , if the parameters are adjusted by an appropriate way , the performance of the force generation method will be improved . In this paper , real-coded genetic algorithm ( RCGA ) , which is an optimization technique , is applied to the parameter optimization of the VDP oscillator because it is easily implementable for nonlinear systems . Simulation results show that RCGA succeeded in finding out sets of better parameters than ones used in the previous study .
This paper presents a Real Coded Genetic Algorithm ( RCGA ) for finding the load flow solution of electrical power systems . The proposed method is based on the minimization of the real and reactive power mismatches at various buses .
The test results of four benchmark functions verify the advantages of the algorithm in global search , convergence speed and avoiding prematurity . In comparison of two case studies with the real genetic algorithm ( RGA ) , the evolution programming ( EP ) , the particle swarm optimization ( PSO ) , the ant colony optimization ( ACO ) , and the standard glowworm swarm optimization ( GSO ) , the MAGSO provides significantly robust solutions of ELD problem of a large-scale hydropower station .
The proposed scheme enhances the grey prediction method of difference equations , which is a single variable second order grey model ( DGM(2,1 ) model ) . It is performed by a real-valued genetic algorithm ( RGA ) with a system performance index as fitness function . We apply the integral of time multiplied by the square error criterion ( ITSE ) to form a suitable fitness function in RGA .
In array pattern synthesis , it is often designed to achieve the desired radiation pattern . In this paper , real-coded genetic algorithm ( RGA ) optimization method is presented to optimize the value of weights of each antenna element to minimize side lobe level of the uniform spaced linear array geometries with a certain main beam width . The optimization program is done by using MATLAB .
In terms of the characteristics of water resource compound system and the conditions of water resources , an evaluation system of sustainable water resource utilization has been established according to the principles of sustainability . Projection pursuit model(PPM ) and real coding based genetic algorithm(RGA ) have been used in the evaluation , which can synthesize the multiple factors into a single pursuit value and attain an optimal scheme . The result shows that it is effective to solve the weighted human interference , and fulfill the evaluation of sustainable water resource utilization by an optimized sequence .
With a kind of improved genetic algorithm(GA)real coding based accelerating genetic algorithm ( RAGA ) , one area 's hydrogeologic parameters have been inverse computed and set up a mathematic model that is coupled with numerical simulation . At the same time , it has been compared with application of real coding genetic algorithm(RGA ) and manual adjust parameters in inverse problem of hydrogeologic parameters . The study shows that RAGA has characters of global optimization and accelerative convergence when compared with the other two ways.
Abstract This paper establishes the utility of a real number genetic algorithm ( RGA ) for direct learning neural control . The controller has a hierazchical structure where the leazning algorithm , RGA , is the higher level and a feedback neural controller is the lower .
Various numerical optimization procedures are then realized to increase the engine power output and simultaneously avoid conditions leading to undesired self-ignitions . These are either based on the use of a non-evolutionary algorithm or employ a genetic algorithm in the case multiple contrasting objectives are set . The response surface methodology is also explored as a way to reduce the computational effort .
In this paper , a novel hybrid approach is proposed for simultaneous feature selection and feature weighting with k value selection of K-NN rule based on Biogeography based optimization ( BBO ) . The 6 evolutionary algorithms and 14 non-evolutionary algorithms are used to compare and evaluate with the novel proposed algorithm ( BBO-KNN ) . The experimental results signify that the BBO-KNN has higher efficiency compared to other methods and is led to higher classification rate as well as effective data dimension reduction .
Symbolic regression is a common application for genetic programming ( GP ) . This paper presents a new non-evolutionary technique for symbolic regression that , compared to competent GP approaches on real-world problems , is orders of magnitude faster ( taking just seconds ) , returns simpler models , has comparable or better prediction on unseen data , and converges reliably and deterministically . I dub the approach FFX , for Fast Function Extraction .
Monte-Carlo Tree Search is a popular method to implement computer programs for board games , and its performance can be significantly improved by including static knowledge about the game , for example in the formof patterns learned from game records . Finding the right pattern shapes is still an open problem , and we propose in this paper an evolutionary-like method to optimize the pattern shapes . We avoid direct optimization through the heavy Monte-Carlo framework by using instead the performance of a machine-learning algorithm as an early indicator of the quality of the pattern shapes .
Streaming media data access has been a problem for several years , and the problem becomes tougher in the mobile environment in which mobile users use mobile devices that are of rather limited storage space , preventing the clients from having a large cache . In this paper , we design a novel evolutionary caching algorithm for base stations to adapt to the user requests , so as to make the scheme more adaptive to the changing environment while maintaining good Byte Hit Ratio ( BHR ) or Number Hit Ratio ( NHR ) for the real world requests . We evaluate the effectiveness of our evolutionary caching algorithm through simulation studies , the results of which demonstrate that our scheme can obtain good performance on buffering streaming media data for user requests as far as the BHR and NHR metrics are concerned .
Abstract The theory of order statistics is utilized in this paper to analyze the convergence velocity ϕ of simplified evolutionary algorithms based on mutation and ( μ + , λ)-selection . A general , representation-independent way of theoretical analysis is outlined and put in concrete terms for two specific objective functions : The binary counting ones objective function and the continuous sphere model .
The proposed multi objective evolutionary method has been applied to the standard IEEE 30 bus six generators , fourteen generators and 40 thermal generating units , respectively , as small , medium and large test power system . The numerical results obtained with the proposed method based on tables and figures compared with other evolutionary algorithm of scientific literatures . The results regards that the proposed CIABC algorithm surpasses the other available methods in terms of computational efficiency and solution quality .
Abstract Evolutionary games usually take into consideration individuals ’ strategies as the transformative characteristic which leads to the evolution of the population . Here , besides the strategies , interaction aspects are also considered as evolutionary attributes which can change over time as the replacement dynamic renovates the population choosing locally better individuals to reproduce .
According to the industrial experts the amount of false positives and false negatives was too high for CouplingViewer to be industrially applicable . Analysis for root causes in this case study confirmed some known and identified some new research directions to improve the performance of evolutionary couplings analysis .
No explicit conditions are imposed on survival : selection criteria are an emergent property of the global system and can not be deduced from local CA rules . As a result , populations undergo self-organized evolutionary processes leading to complex and unpredictable spatial and temporal dynamics on multiple scales . The study of these complex dynamics forms the primary focus of this thesis .
In the study of innovation theory , some scholars believe that innovation process is the process of knowledge evolution . Cluster innovation is a complex economic activity based on knowledge resource , It is systematic , procedure , and with the evolutionary process of innovation self-organized . In this process , the knowledge structures of cluster innovation alternate by the path of " stability-bifurcation-stability " , and it also contains the gradual improvement of the structure of innovation knowledge linked to learning and the breakthrough innovation of such structure associated with variation .
This search within a bounded space can be compared to the purposeful , constrained decision making process involved in designing . The generated solutions use the evolutionary concept of genetic algorithms to deduce solutions within the predefined design solution search space . Simple evolutionary concepts such as reproduction , crossover and mutation aid this search process .
The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document , handling rich features of text data and dealing with the well known problem of the curse of dimensionality . In this thesis , we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods – Query Sense Clustering ( QSC ) , Dirichlet Weighted K-means ( DWKM ) , Multi-View Multi-Objective Evolutionary Algorithm ( MMOEA ) , Multi-objective Document Clustering ( MDC ) and Multi-Objective MultiView Ensemble Clustering ( MOMVEC ) . These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods .
Network expansion planning with microgrid aggregators under uncertainty Enhancing the Power System LoadabilityUsing TCSC: Improved Gravitational SearchAlgorithm A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids Gravitational Search-Based Effective Knowledge Discovery Process for Spatio- Temporal Databases A study on intrusion detection using neural networks trained with evolutionary algorithms An Improved Rider Optimization Algorithm for Solving Engineering Optimization Problems A Gravitational Artificial Bee Colony Optimization Algorithm and Application An effective bacterial foraging optimizer for global optimization Gravitational Search Algorithms in Data Mining: A Survey State monitoring of induced draft fan in thermal power plant by gravitational searching algorithm optimized BP neural network Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System Gravitational search assisted algorithm for TCSC placement for congestion control in deregulated power system Hierarchy Style Application in Line Extension with Responsive Loads Evaluating the Dynamic Nature of Solar Units Adaptive geometric angle-based algorithm with independent objective biasing for pruning Pareto-optimal solutions An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization Evolutionary Search with Multiple Utopian Reference Points in Decomposition-Based Multiobjective Optimization A stopping criterion for decomposition-based multi-objective evolutionary algorithms Decomposing the user-preference in multiobjective optimization A MOEA/D based approach for hospital department layout design Modified multiobjective evolutionary algorithm based on decomposition for low-carbon scheduling of distributed permutation flow-shop Running Time Analysis of MOEA/D on Pseudo-Boolean Functions. A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design An Improved Ideal Point Setting in Multiobjective Evolutionary Algorithm Based on Decomposition "Gridifying" an Evolutionary Algorithm for Inference of Genetic Networks Using the Improved GOGA Framework and Its Performance Evaluation on OBI Grid Using A Priori Knowledge after Genetic Network Inference: Integrating Multiple Kinds of Knowledge A Modular Gene Regulation Network Model of Artificial Ontogenesis Creating stock trading rules using graph-based estimation of distribution algorithm Comparison of approaches for machine‐learning optimization of neural networks for detecting gene‐gene interactions in genetic epidemiology Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems A System for Mining Generalized Association Rules with Ontology Using Genetic Network Programming Association Rule Mining with Chi-Squared Test Using Alternate Genetic Network Programming A Study of Assigning Proper Roles to Multiple Agents with Genetic Network Programming Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers Asymmetric Trust and Causal Reasoning in Blockchain-based AIs Weeding manipulator exploiting its oscillatory motion for force generation: Parameter optimization of VDP oscillator using real-coded genetic algorithm Load Flow Analysis Using Real Coded Genetic Algorithm Economic load dispatch of renewable energy-based power systems with high penetration of large-scale hydropower station based on multi-agent glowworm swarm optimization Optimal Fuzzy Switching Grey Prediction with RGA for TRMS Control Synthesis of Linear Antenna Array Using Genetic Algorithm to Control Side Lobe Level Application of Projection Pursuit Model in Sustainable Water Resource Utilization in the Western Songnen Plain Application of Real Coding Based on Accelerating Genetic Algorithm in Inverse Problem of Hydrogeologic Parameters Direct Learning Neural Controller Tuned by a Real Number Genetic Algorithm Optimization of a GDI engine operation in the absence of knocking through numerical 1D and 3D modeling Simultaneous feature selection and feature weighting with K selection for KNN classification using BBO algorithm FFX: Fast, Scalable, Deterministic Symbolic Regression Technology Fast Optimization of the Pattern Shapes in Board Games with Simulated Annealing An evolution-based cache scheme for scalable mobile data access Order Statistics for Convergence Velocity Analysis of Simplified Evolutionary Algorithms A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch Interaction characteristics as evolutionary features for the spatial Prisoner’s Dilemma in a population modeled by continuous probabilistic cellular automata and evolutionary algorithm Transferring Evolutionary Couplings to Industry EMERGENT EVOLUTIONARY DYNAMICS OF SELF-REPRODUCING CELLULAR AUTOMATA Study on the Self-Organization Evolutionary Process of Cluster Innovation Based on the Theory of Knowledge Flow Generative methods and the design process: A design tool for conceptual settlement planning Improving Clustering Methods By Exploiting Richness Of Text Data
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GECCO CEC SSCI LSGRID IEEE Congress on Evolutionary Computation Industrial Conference on Data Mining RSCTC ROBIO SMC KSE Infoscale FOGA
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