In the past decades , different kinds of metaheuristic optimization algorithms [ 1 , 2 ] have been developed ; Simulated Annealing ( SA ) [ 3 , 4 ] , Evolutionary Algorithms ( EAs ) [ 5–7 ] , Differential Evolution ( DE ) [ 8 , 9 ] , Particle Swarm Optimization ( PSO ) [ 10 , 11 ] , Ant Colony Optimization ( ACO ) [ 12 , 13 ] , and Estimation of Distribution Algorithms ( EDAs ) [ 14 , 15 ] are just a few of them . These algorithms have shown excellent search abilities but often lose their efficacy when applied to large and complex problems , e.g. , problem instances with high dimensions , such as those with more than one hundred decision variables . The proposed SABEA adopts two bounding strategies , namely fitness-based bounding and probabilistic sampling-based bounding , to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency . To evaluate the performance of this method , SABEA is applied on top of the classic differential evolution ( DE ) algorithm and a DE variant , and SABEA is compared to a state-of-the-art Distribution-based Adaptive Bounding Genetic Algorithm ( DABGA ) on a set of 27 selected benchmark functions . The results show that SABEA can be used as a complementary strategy for further enhancing the performance of existing evolutionary algorithms and it also outperforms DABGA . Control parameters and mutation methods impact upon the global search ability of differential evolution algorithm ( DE ) , and varying optimization issues own varying parameter settings . In this paper , an enhanced elite archive mutation strategy with self-adaption parameter adjustment ( EAMSADE ) is proposed to raise DE ’s performance . Six benchmark functions have been employed to test the performance of HRO . In addition , particle swarm optimization algorithm ( PSO ) , differential evolution algorithm(DE ) , genetic algorithm ( GA ) , artificial bee colony ( ABC ) are also carried out to compare with the HRO in the same running environment . Experimental results on some benchmark problems verifies that HRO outperforms other optimization algorithms involved in the paper with respect to convergence and the speed of calculation on the whole . We introduce the application of the differential evolution algorithm ( DE ) to optimize the positive Lyapunov exponent in a multi-scroll chaotic oscillator based on saturated nonlinear function series . The positive Lyapunov exponent is optimized from two to nine scrolls by sweeping the coefficients of the chaotic oscillator . Evolutionary Algorithms ( EAs ) , in their traditional form or in combination as in Memetic Algorithms ( MAs ) , have been quite successful in solving a variety of optimization problems in the past . More recently , several excellent Differential Evolution ( DE ) based algorithms have been proposed which have had outstanding success in IEEE Congress on Evolutionary Computation ( CEC ) competition problems . Inspired by previous studies , we propose an algorithm combining the strengths of EA , DE and MA in this paper . The problem is solved first with a mathematical programming technique called the goal attainment method . Based on some shortcomings found , we propose a differential evolution (DE)-based approach to solve the aforementioned problem . The performance of both approaches ( goal attainment and the modified DE ) are compared and discussed , based on quality , robustness , computational time and implementation complexity . Genetic algorithms ( GAs ) are classical evolutionary computation methods , which have a wild application prospect . This paper proposes an improved genetic algorithm , named the isoline genetic algorithm ( IGA ) , for numerical optimization . Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems . We aim at designing advanced optimization algorithms , with inspiration from human ’s creative problem-solving strategies . ZECHMAN , EMILY MICHELLE . Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction . ( Under the direction of S. Ranji Ranjithan . ) Abstract Genetic programming ( GP ) is an evolutionary computation paradigm for automatic program induction . GP has produced impressive results but it still needs to overcome some practical limitations , particularly its high computational cost , overfitting and excessive code growth . Evolutionary Computation paradigm becomes very popular . A number ofapproaches that exploit the biological evolution ability to adaptation arise . Anticipation consists of the estimation from present data in order to react to a future environment situation . This duality is expressed in the Evolutionary Computation paradigm by the precise location of the consideration of present data in the computation of the individuals fitness function . In this paper we consider several instances of Evolutionary Algorithms applied to precise problem and perform an experiment that test t ... Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified . We show that these random fluctuations carry within them valuable information about the underlying genetic network . Far from being a nuisance , the ever-present cellular noise acts as a rich source of excitation that , when processed through a gene network , carries its distinctive fingerprint that encodes a wealth of information about that network . RESULTS We propose a scheme-based on random searches over a parameter space , according to criteria set by qualitative experimental observations-for inferring quantitative parameters from qualitative experimental results . We used five mutant constraints to construct genetic network models for sensory organ precursor formation in Drosophila development . Most of the models were capable of generating expression patterns for the gene Enhancer of split that were compatible with experimental observations for wild type and two Notch mutants . According to the knowledge of brain science , it is suggested that there exists cerebral functional localization , which means that a specific part of the cerebrum is activated depending on various kinds of information human receives . The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming ( GNP ) , a new evolutionary computation method recently developed . GNP has a directed graph structure suitable for realizing functional localization . Artificial Intelligence applications in educational field are getting more and more popular during the last decade ( 1999 - 2009 ) and that is why much relevant research has been conducted . In this paper , we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic , neural networks , genetic programming and hybrid approaches such as neuro – fuzzy systems and genetic programming neural networks ( GPNN ) in student modeling . 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 . In this paper , we propose a genetic network programming based method to mine equalized association rules in multi concept layers of ontology . We first introduce ontology to facilitate building the multi concept layers and propose dynamic threshold approach ( DTA ) to equalize the different layers . In this paper , we propose a genetic network programming based method to mine generalized association rules with ontology . We first introduce ontology to facilitate building the multi concept layers and propose dynamic threshold approach ( DTA ) to equalize the different layers . Generally , regularization of solutions by " smoothness constraint " is an addition in the classical approaches to cater to the sensitivity of solutions for small perturbations . In this paper , two well-known evolutionary computation ( EC ) algorithms : 1 ) the genetic algorithm ( GA ) with binary and real encoding schemes , and 2 ) the particle swarm algorithm ( PSO ) , are proposed to evolve the estimated image in order to obtain an optimal solution for restoration with adaptive regularization . Thus the restoration framework presented in this paper is new and novel , such that it reconstructs the image guided by evolutionary computations . This paper investigates global optimization methods from the perspective of population-based and restarted point-based heuristics . We examine the performance of a standard evolutionary computation ( EC ) methodology , a derivative-based sequential quadratic programming ( SQP ) algorithm and a novel derivative-free stochastic coordinate ascent ( SCA ) algorithm . All methods are analyzed by random sampling of the feasible search space . There are two types of interaction that can exist among decision variables : inseparable function interaction and variable dependence . The aim of this paper is to propose an Evolutionary Computing ( EC ) technique for handling variable dependence in multi-objective optimisation problems . In spite of its immense potential for real-life problems , lack of systematic research has plagued this field for a long time . Based on evolutionary computation ( EC ) concepts , we developed an adaptive evolutionary planner/navigator ( EP/N ) as a novel approach to path planning and navigation . The EP/N is characterized by generality , flexibility , and adaptability . Based on evolutionary computation ( EC ) concepts , we developed an evolutionary navigator ( EN ) as an approach to path planning and navigation . The EN is characterized by generality , flexibility and adaptability . However , the inclusion of the strategy in the Portfolio Optimization problem model yields a complex constraint that is difficult to handle using traditional methods , justifying a compelling need to look for heuristic solutions . In this paper we discuss an Evolutionary Computation ( EC ) based solution for an integrated optimization of long-short portfolios , when the Risk Budgeting strategy is incorporated in the problem model , besides inclusion of constraints reflective of investor preferences . Two EC based strategies viz . , Evolution Strategy with Hall of Fame and Differential Evolution ( rand/1/bin ) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained . There is little guidance available to help practitioners deal with this issue . Most evolutionary computation ( EC ) theory is only applicable to specific representations , or assumes knowledge of the fitness function , such as the location of optima . We are developing an approach based on theory from the biology community to address this problem . This paper describes a new technique for automatically developing Artificial Neural Networks ( ANNs ) by means of an Evolutionary Computation ( EC ) tool , called Genetic Programming ( GP ) . This paper also describes a practical application in the field of Data Mining . Different techniques for the computation of the pass-to-pass accuracy performance have been used , including a procedure to measure live in the field and a post-processing alternative . The diversity of scenarios available allows drawing conclusions on the applicability of EDAS DGPS corrections ( in terms of maximum distance from the target EGNOS station ) for precision agriculture and also ANNUAL OF NAVIGATION 26/2019 47 understanding the impact of operationally relevant aspects such as the quality of the mobile internet coverage ( highly variable across Europe ) . The EDAS system and its architecture , the main types of data disseminated through EDAS services and the online information available to the EDAS users are introduced in this paper . Recently , a new approach to EDAs has been developed , based on copula theory , to improve the estimation of the joint probability distribution function . However , most copula-based EDAs still present two major drawbacks : focus on copulas with constant parameters , and premature convergence . This paper presents a new copula-based estimation of distribution algorithm for numerical optimization problems , named EDA based on Multivariate Elliptical Copulas ( EDA-MEC ) . This model uses multivariate copulas to estimate the probability distribution for generating a population of individuals . The EDA-MEC differs from other copula-based EDAs in several aspects : the copula parameter is dynamically estimated , using dependence measures ; it uses a variation of the learned probability distribution to generate individuals that help to avoid premature convergence ; and uses a heuristic to reinitialize the population as an additional technique to preserve the diversity of solutions . The paper shows , by means of a set of parametric tests , that this approach improves the overall performance of the optimization process when compared with other copula-based EDAs and with other efficient heuristics such as the Covariance Matrix Adaptation Evolution Strategy ( CMA-ES ) . Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables . Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20 % of the benchmark functions . According to the appraisal score , the preference order or the most suitable alternative can be ranked . Finally , the feasibility and efficiency of the extended EDAS is demonstrated by through the example . However , there is no clear evidence that it can handle these trade-offs optimally to add value to such a complex and dynamic network . We present a scenario-based approach to recognize and evaluate typical security trade-off situations in the IoT. Using the Event-driven Adaptive Security ( EDAS ) model , we provide the assessment of dynamic trade-off decisions in the IoT. We have showed that an optimum trade-off mitigation response in the IoT can be automated by assessing various contextual requirements , such as the QoS and user preferences , thing capabilities , and the risk faced , at runtime . eHealth scenarios are examined to illustrate system application in IoT-based remote patient monitoring systems . Analyzing a part of a situation may lead to false alarms and later to unnecessary and incorrect configurations . To overcome these concerns , we proposed an event-driven adaptive security ( EDAS ) model for IoT. EDAS aims to observe security events ( changes ) generated by various things in the monitored IoT environment , investigates any intentional or unintentional risks associated with the events and adapts to it autonomously . It correlates different events in time and space to reduce any false alarms and provides a mechanism to predict attacks before they are realized . In this paper , we propose using kernels of Mallows models under the Kendall's-tau and Cayley distances within EDAs . In order to demonstrate the validity of this new algorithm , Mallows Kernel EDA , we compare its performance with the classical Mallows and Generalized Mallows EDAs , on a benchmark of 90 instances of two different types of permutation problems : the quadratic assignment problem and the permutation flowshop scheduling problem . Experimental results reveal that , in most cases , Mallows Kernel EDA outperforms the Mallows and Generalized Mallows EDAs under the same distance . In this paper , we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam . In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall , Cayley and Ulam distances , we run them on a benchmark of 120 instances from four well known permutation problems . The conducted experiments showed that there is not just one metric that performs the best in all the problems . This present work proposes a novel fuzzy-based multi-criteria decision-making ( MCDM ) approach for addressing material selection problem under uncertain environment . The integrated framework integrates fuzzy decision making with evaluation based on distance from average solution ( EDAS ) MCDM method . Proposed framework fulfills the gap of restricted exploration of fuzzy decision-making tools in the domain of material design . Abstract The purpose of this paper is proposing , analyzing and assessing two new algorithms of the EDAS method for group multi-criteria decision making with fuzzy sets . In the first proposed EDAS extension for distance measure between two interval Type-2 fuzzy numbers is applied Graded Mean Integration Representation ( GMIR ) . The second algorithm takes into account the proximity between the fuzzy alternatives and its similarity measure is Map Distance Operator ( MDO ) . In this paper , we construct picture fuzzy EDAS model based on traditional EDAS ( Evaluation based on Distance from Average Solution ) model . Firstly , we briefly review the definition of picture fuzzy sets ( PFSs ) and introduce the score function , accuracy function and operational laws of picture fuzzy numbers ( PFNs ) . Using these local searches , various meta-heuristics can be generalized to deal with tree data structures to introduce a more general framework of meta-heuristics called Meta-Heuristics Programming ( MHP ) as general machine learning tools . As an alternative to Genetic Programming ( GP ) algorithm , Memetic Programming ( MP ) algorithm is proposed as a new outcome of the MHP framework . The efficiency of the proposed MP Algorithm is examined through comparative numerical experiments . However , to date there is no known formula that produces all primes . In this article , we use a hybrid evolutionary algorithm , called the memetic programming ( MP ) algorithm , to generate mathematical formulas that produce distinct primes . Using the MP algorithm , we succeeded in discovering an interesting set of formulas that produce sets of distinct primes . In the present study , a Novel Memetic Genetic Algorithm ( NMGA ) is developed to solve the Traveling Salesman Problem ( TSP ) . The proposed NMGA is the combination of Boltzmann probability selection and a multi-parent crossover technique with known random mutation . VFS-HS employs a novel model-management strategy that uses a multi-level screening mechanism based on non-dominated sorting to strictly control the numbers of low-fidelity and high-fidelity evaluations and to keep a balance between exploration and exploitation . The performance of VFS-HS is validated through comparison not only to those of four single-fidelity surrogate-assisted optimization methods ( i.e. the particle swarm optimization algorithm with radial basis function-based surrogate ( OPUS-RBF ) , the two-layer surrogate-assisted particle swarm optimization algorithm ( TLSAPSO ) , the surrogate-assisted hierarchical particle swarm optimization ( SHPSO ) and the hybrid surrogate-based optimization using space reduction ( HSOSR ) ) but also to that a multi-fidelity surrogate-assisted optimization method ( the multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution ( MGPMDE ) ) on the CEC2014 expensive optimization test suite . A real-world problem of the optimal design for a long cylindrical gas-pressure vessel is also investigated . To address it , the two major contributions of this paper include : ( 1 ) development of a new multi-fidelity surrogate-model-based optimization framework , which substantially improves reliability and efficiency of optimization compared to many existing methods , and ( 2 ) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models . A new efficient global optimization method is then proposed , referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution . Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem . Our experimental results show that the block insert with the first strategy often outperforms other local search schemes within the same computational time . To further improve the performance of this local search scheme , we incorporate it into the iterated local search and genetic algorithm frameworks , and develop the block-insertion-based iterated local search ( ILSb ) and memetic algorithm ( MAb ) , respectively . The computational results show that both the ILSb and MAb outperform the state-of-the-art meta-heuristics . However , it usually lacks of an efficient local search capability , and may sometimes suffer from weak convergence . In this paper , a memetic HS method , m-HS , with local search function is proposed and studied . The local search in the m-HS is inspired by the principle of bee foraging , and performs only at selected harmony memory members , which can significantly improve the efficiency of the overall search procedure . However , it usually lacks of an efficient local search capability , and may sometimes suffer from weak convergence . In this paper , a memetic HS method , m-HS , with local search function is proposed and studied . The local search in the m-HS is based on the principle of bee foraging like strategy , and performs only at selected harmony memory members , which can significantly improve the efficiency of the overall search procedure . Original and biogeographical memetic algorithms for solving transcomputational optimization problem are presented for the traveling salesman problem . Authors presented the method of biogeography and its modifications , as well as results of the comparative analysis of genetic , biogeographic and memetic algorithms . Experiments were carried out on certain benchmarks from the library TSPLIB . Original biogeographical and memetic algorithms for solving trans computational optimization problem are presented for the traveling salesman problem . Authors presented the method of biogeography and its modifications , as well as results of the comparative analysis of genetic , biogeographic and memetic algorithms . The authors conducted experimental verification of the effectiveness of the algorithms on known test functions . The key challenge in DSPR is how to simultaneously keep track changes and search for the global optima . A multi-memory based multi-population memetic algorithm is proposed for DSPR in this paper . The proposed algorithm combines the strength of three different strategies , multi-memory , multi-population and memetic algorithm , aiming to effectively explore and exploit the search space . Superiority of the new method is demonstrated by comparing it with three classification methods including Linear Discriminant Analysis , K-Nearest Neighbor and Support Vector Machine-Radial Basis Function . Self-Adaptive Differential Evolution , Differential Evolution/current-to-best/l , Particle Swarm Optimization and Genetic Algorithm-based clustering approaches have also been used here to study the relative performance of the proposed adaptive memetic algorithm-based clustering technique with respect to runtime and classification accuracy . The supervised and unsupervised feature selection methods were investigated . To address the problems of optimizing the supervised feature selection methods so as to improve document clustering , memetic hybridization between filter and wrapper feature selection , known as Memetic Algorithm Feature Selection , was presented first . In order to deal with the unlabelled features , unsupervised feature selection method was also proposed . A Review and Progress Report . Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search ( LS ) is coadapted alongside candidate solutions within a hybrid evolutionary system . Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems . To prove this theorem we show that any base $ \alpha$ , dimension l code word will cycle in O(lq ) iterations of this Gray code mapping , and that this upper bound is attained . This theorem is a generalization of a theorem proven by Culberson [ Evolutionary Comput . , 2 ( 1995 ) , pp . 279 - -311 ] for the binary case . A parallel solution to the implementation of evolutionary algorithms is proposed , where the most costly part of the whole evolutionary algorithm computations ( the population evaluation ) , is deported to a GPGPU card . Experiments are presented for two benchmark examples on two models of GPGPU cards : first a " toy " problem is used to illustrate some noticable behaviour characteristics before a real problem is tested out .
In the past decades , different kinds of metaheuristic optimization algorithms [ 1 , 2 ] have been developed ; Simulated Annealing ( SA ) [ 3 , 4 ] , Evolutionary Algorithms ( EAs ) [ 5–7 ] , Differential Evolution ( DE ) [ 8 , 9 ] , Particle Swarm Optimization ( PSO ) [ 10 , 11 ] , Ant Colony Optimization ( ACO ) [ 12 , 13 ] , and Estimation of Distribution Algorithms ( EDAs ) [ 14 , 15 ] are just a few of them . These algorithms have shown excellent search abilities but often lose their efficacy when applied to large and complex problems , e.g. , problem instances with high dimensions , such as those with more than one hundred decision variables .
The proposed SABEA adopts two bounding strategies , namely fitness-based bounding and probabilistic sampling-based bounding , to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency . To evaluate the performance of this method , SABEA is applied on top of the classic differential evolution ( DE ) algorithm and a DE variant , and SABEA is compared to a state-of-the-art Distribution-based Adaptive Bounding Genetic Algorithm ( DABGA ) on a set of 27 selected benchmark functions . The results show that SABEA can be used as a complementary strategy for further enhancing the performance of existing evolutionary algorithms and it also outperforms DABGA .
Control parameters and mutation methods impact upon the global search ability of differential evolution algorithm ( DE ) , and varying optimization issues own varying parameter settings . In this paper , an enhanced elite archive mutation strategy with self-adaption parameter adjustment ( EAMSADE ) is proposed to raise DE ’s performance .
Six benchmark functions have been employed to test the performance of HRO . In addition , particle swarm optimization algorithm ( PSO ) , differential evolution algorithm(DE ) , genetic algorithm ( GA ) , artificial bee colony ( ABC ) are also carried out to compare with the HRO in the same running environment . Experimental results on some benchmark problems verifies that HRO outperforms other optimization algorithms involved in the paper with respect to convergence and the speed of calculation on the whole .
We introduce the application of the differential evolution algorithm ( DE ) to optimize the positive Lyapunov exponent in a multi-scroll chaotic oscillator based on saturated nonlinear function series . The positive Lyapunov exponent is optimized from two to nine scrolls by sweeping the coefficients of the chaotic oscillator .
Evolutionary Algorithms ( EAs ) , in their traditional form or in combination as in Memetic Algorithms ( MAs ) , have been quite successful in solving a variety of optimization problems in the past . More recently , several excellent Differential Evolution ( DE ) based algorithms have been proposed which have had outstanding success in IEEE Congress on Evolutionary Computation ( CEC ) competition problems . Inspired by previous studies , we propose an algorithm combining the strengths of EA , DE and MA in this paper .
The problem is solved first with a mathematical programming technique called the goal attainment method . Based on some shortcomings found , we propose a differential evolution (DE)-based approach to solve the aforementioned problem . The performance of both approaches ( goal attainment and the modified DE ) are compared and discussed , based on quality , robustness , computational time and implementation complexity .
Genetic algorithms ( GAs ) are classical evolutionary computation methods , which have a wild application prospect . This paper proposes an improved genetic algorithm , named the isoline genetic algorithm ( IGA ) , for numerical optimization .
Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems . We aim at designing advanced optimization algorithms , with inspiration from human ’s creative problem-solving strategies .
ZECHMAN , EMILY MICHELLE . Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction . ( Under the direction of S. Ranji Ranjithan . )
Abstract Genetic programming ( GP ) is an evolutionary computation paradigm for automatic program induction . GP has produced impressive results but it still needs to overcome some practical limitations , particularly its high computational cost , overfitting and excessive code growth .
Evolutionary Computation paradigm becomes very popular . A number ofapproaches that exploit the biological evolution ability to adaptation arise .
Anticipation consists of the estimation from present data in order to react to a future environment situation . This duality is expressed in the Evolutionary Computation paradigm by the precise location of the consideration of present data in the computation of the individuals fitness function . In this paper we consider several instances of Evolutionary Algorithms applied to precise problem and perform an experiment that test t ...
Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified . We show that these random fluctuations carry within them valuable information about the underlying genetic network . Far from being a nuisance , the ever-present cellular noise acts as a rich source of excitation that , when processed through a gene network , carries its distinctive fingerprint that encodes a wealth of information about that network .
RESULTS We propose a scheme-based on random searches over a parameter space , according to criteria set by qualitative experimental observations-for inferring quantitative parameters from qualitative experimental results . We used five mutant constraints to construct genetic network models for sensory organ precursor formation in Drosophila development . Most of the models were capable of generating expression patterns for the gene Enhancer of split that were compatible with experimental observations for wild type and two Notch mutants .
According to the knowledge of brain science , it is suggested that there exists cerebral functional localization , which means that a specific part of the cerebrum is activated depending on various kinds of information human receives . The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming ( GNP ) , a new evolutionary computation method recently developed . GNP has a directed graph structure suitable for realizing functional localization .
Artificial Intelligence applications in educational field are getting more and more popular during the last decade ( 1999 - 2009 ) and that is why much relevant research has been conducted . In this paper , we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic , neural networks , genetic programming and hybrid approaches such as neuro – fuzzy systems and genetic programming neural networks ( GPNN ) in student modeling . 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 .
In this paper , we propose a genetic network programming based method to mine equalized association rules in multi concept layers of ontology . We first introduce ontology to facilitate building the multi concept layers and propose dynamic threshold approach ( DTA ) to equalize the different layers .
In this paper , we propose a genetic network programming based method to mine generalized association rules with ontology . We first introduce ontology to facilitate building the multi concept layers and propose dynamic threshold approach ( DTA ) to equalize the different layers .
Generally , regularization of solutions by " smoothness constraint " is an addition in the classical approaches to cater to the sensitivity of solutions for small perturbations . In this paper , two well-known evolutionary computation ( EC ) algorithms : 1 ) the genetic algorithm ( GA ) with binary and real encoding schemes , and 2 ) the particle swarm algorithm ( PSO ) , are proposed to evolve the estimated image in order to obtain an optimal solution for restoration with adaptive regularization . Thus the restoration framework presented in this paper is new and novel , such that it reconstructs the image guided by evolutionary computations .
This paper investigates global optimization methods from the perspective of population-based and restarted point-based heuristics . We examine the performance of a standard evolutionary computation ( EC ) methodology , a derivative-based sequential quadratic programming ( SQP ) algorithm and a novel derivative-free stochastic coordinate ascent ( SCA ) algorithm . All methods are analyzed by random sampling of the feasible search space .
There are two types of interaction that can exist among decision variables : inseparable function interaction and variable dependence . The aim of this paper is to propose an Evolutionary Computing ( EC ) technique for handling variable dependence in multi-objective optimisation problems . In spite of its immense potential for real-life problems , lack of systematic research has plagued this field for a long time .
Based on evolutionary computation ( EC ) concepts , we developed an adaptive evolutionary planner/navigator ( EP/N ) as a novel approach to path planning and navigation . The EP/N is characterized by generality , flexibility , and adaptability .
Based on evolutionary computation ( EC ) concepts , we developed an evolutionary navigator ( EN ) as an approach to path planning and navigation . The EN is characterized by generality , flexibility and adaptability .
However , the inclusion of the strategy in the Portfolio Optimization problem model yields a complex constraint that is difficult to handle using traditional methods , justifying a compelling need to look for heuristic solutions . In this paper we discuss an Evolutionary Computation ( EC ) based solution for an integrated optimization of long-short portfolios , when the Risk Budgeting strategy is incorporated in the problem model , besides inclusion of constraints reflective of investor preferences . Two EC based strategies viz . , Evolution Strategy with Hall of Fame and Differential Evolution ( rand/1/bin ) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained .
There is little guidance available to help practitioners deal with this issue . Most evolutionary computation ( EC ) theory is only applicable to specific representations , or assumes knowledge of the fitness function , such as the location of optima . We are developing an approach based on theory from the biology community to address this problem .
This paper describes a new technique for automatically developing Artificial Neural Networks ( ANNs ) by means of an Evolutionary Computation ( EC ) tool , called Genetic Programming ( GP ) . This paper also describes a practical application in the field of Data Mining .
Different techniques for the computation of the pass-to-pass accuracy performance have been used , including a procedure to measure live in the field and a post-processing alternative . The diversity of scenarios available allows drawing conclusions on the applicability of EDAS DGPS corrections ( in terms of maximum distance from the target EGNOS station ) for precision agriculture and also ANNUAL OF NAVIGATION 26/2019 47 understanding the impact of operationally relevant aspects such as the quality of the mobile internet coverage ( highly variable across Europe ) . The EDAS system and its architecture , the main types of data disseminated through EDAS services and the online information available to the EDAS users are introduced in this paper .
Recently , a new approach to EDAs has been developed , based on copula theory , to improve the estimation of the joint probability distribution function . However , most copula-based EDAs still present two major drawbacks : focus on copulas with constant parameters , and premature convergence . This paper presents a new copula-based estimation of distribution algorithm for numerical optimization problems , named EDA based on Multivariate Elliptical Copulas ( EDA-MEC ) . This model uses multivariate copulas to estimate the probability distribution for generating a population of individuals . The EDA-MEC differs from other copula-based EDAs in several aspects : the copula parameter is dynamically estimated , using dependence measures ; it uses a variation of the learned probability distribution to generate individuals that help to avoid premature convergence ; and uses a heuristic to reinitialize the population as an additional technique to preserve the diversity of solutions . The paper shows , by means of a set of parametric tests , that this approach improves the overall performance of the optimization process when compared with other copula-based EDAs and with other efficient heuristics such as the Covariance Matrix Adaptation Evolution Strategy ( CMA-ES ) .
Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables . Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20 % of the benchmark functions .
According to the appraisal score , the preference order or the most suitable alternative can be ranked . Finally , the feasibility and efficiency of the extended EDAS is demonstrated by through the example .
However , there is no clear evidence that it can handle these trade-offs optimally to add value to such a complex and dynamic network . We present a scenario-based approach to recognize and evaluate typical security trade-off situations in the IoT. Using the Event-driven Adaptive Security ( EDAS ) model , we provide the assessment of dynamic trade-off decisions in the IoT. We have showed that an optimum trade-off mitigation response in the IoT can be automated by assessing various contextual requirements , such as the QoS and user preferences , thing capabilities , and the risk faced , at runtime . eHealth scenarios are examined to illustrate system application in IoT-based remote patient monitoring systems .
Analyzing a part of a situation may lead to false alarms and later to unnecessary and incorrect configurations . To overcome these concerns , we proposed an event-driven adaptive security ( EDAS ) model for IoT. EDAS aims to observe security events ( changes ) generated by various things in the monitored IoT environment , investigates any intentional or unintentional risks associated with the events and adapts to it autonomously . It correlates different events in time and space to reduce any false alarms and provides a mechanism to predict attacks before they are realized .
In this paper , we propose using kernels of Mallows models under the Kendall's-tau and Cayley distances within EDAs . In order to demonstrate the validity of this new algorithm , Mallows Kernel EDA , we compare its performance with the classical Mallows and Generalized Mallows EDAs , on a benchmark of 90 instances of two different types of permutation problems : the quadratic assignment problem and the permutation flowshop scheduling problem . Experimental results reveal that , in most cases , Mallows Kernel EDA outperforms the Mallows and Generalized Mallows EDAs under the same distance .
In this paper , we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam . In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall , Cayley and Ulam distances , we run them on a benchmark of 120 instances from four well known permutation problems . The conducted experiments showed that there is not just one metric that performs the best in all the problems .
This present work proposes a novel fuzzy-based multi-criteria decision-making ( MCDM ) approach for addressing material selection problem under uncertain environment . The integrated framework integrates fuzzy decision making with evaluation based on distance from average solution ( EDAS ) MCDM method . Proposed framework fulfills the gap of restricted exploration of fuzzy decision-making tools in the domain of material design .
Abstract The purpose of this paper is proposing , analyzing and assessing two new algorithms of the EDAS method for group multi-criteria decision making with fuzzy sets . In the first proposed EDAS extension for distance measure between two interval Type-2 fuzzy numbers is applied Graded Mean Integration Representation ( GMIR ) . The second algorithm takes into account the proximity between the fuzzy alternatives and its similarity measure is Map Distance Operator ( MDO ) .
In this paper , we construct picture fuzzy EDAS model based on traditional EDAS ( Evaluation based on Distance from Average Solution ) model . Firstly , we briefly review the definition of picture fuzzy sets ( PFSs ) and introduce the score function , accuracy function and operational laws of picture fuzzy numbers ( PFNs ) .
Using these local searches , various meta-heuristics can be generalized to deal with tree data structures to introduce a more general framework of meta-heuristics called Meta-Heuristics Programming ( MHP ) as general machine learning tools . As an alternative to Genetic Programming ( GP ) algorithm , Memetic Programming ( MP ) algorithm is proposed as a new outcome of the MHP framework . The efficiency of the proposed MP Algorithm is examined through comparative numerical experiments .
However , to date there is no known formula that produces all primes . In this article , we use a hybrid evolutionary algorithm , called the memetic programming ( MP ) algorithm , to generate mathematical formulas that produce distinct primes . Using the MP algorithm , we succeeded in discovering an interesting set of formulas that produce sets of distinct primes .
In the present study , a Novel Memetic Genetic Algorithm ( NMGA ) is developed to solve the Traveling Salesman Problem ( TSP ) . The proposed NMGA is the combination of Boltzmann probability selection and a multi-parent crossover technique with known random mutation .
VFS-HS employs a novel model-management strategy that uses a multi-level screening mechanism based on non-dominated sorting to strictly control the numbers of low-fidelity and high-fidelity evaluations and to keep a balance between exploration and exploitation . The performance of VFS-HS is validated through comparison not only to those of four single-fidelity surrogate-assisted optimization methods ( i.e. the particle swarm optimization algorithm with radial basis function-based surrogate ( OPUS-RBF ) , the two-layer surrogate-assisted particle swarm optimization algorithm ( TLSAPSO ) , the surrogate-assisted hierarchical particle swarm optimization ( SHPSO ) and the hybrid surrogate-based optimization using space reduction ( HSOSR ) ) but also to that a multi-fidelity surrogate-assisted optimization method ( the multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution ( MGPMDE ) ) on the CEC2014 expensive optimization test suite . A real-world problem of the optimal design for a long cylindrical gas-pressure vessel is also investigated .
To address it , the two major contributions of this paper include : ( 1 ) development of a new multi-fidelity surrogate-model-based optimization framework , which substantially improves reliability and efficiency of optimization compared to many existing methods , and ( 2 ) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models . A new efficient global optimization method is then proposed , referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution . Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem .
Our experimental results show that the block insert with the first strategy often outperforms other local search schemes within the same computational time . To further improve the performance of this local search scheme , we incorporate it into the iterated local search and genetic algorithm frameworks , and develop the block-insertion-based iterated local search ( ILSb ) and memetic algorithm ( MAb ) , respectively . The computational results show that both the ILSb and MAb outperform the state-of-the-art meta-heuristics .
However , it usually lacks of an efficient local search capability , and may sometimes suffer from weak convergence . In this paper , a memetic HS method , m-HS , with local search function is proposed and studied . The local search in the m-HS is inspired by the principle of bee foraging , and performs only at selected harmony memory members , which can significantly improve the efficiency of the overall search procedure .
However , it usually lacks of an efficient local search capability , and may sometimes suffer from weak convergence . In this paper , a memetic HS method , m-HS , with local search function is proposed and studied . The local search in the m-HS is based on the principle of bee foraging like strategy , and performs only at selected harmony memory members , which can significantly improve the efficiency of the overall search procedure .
Original and biogeographical memetic algorithms for solving transcomputational optimization problem are presented for the traveling salesman problem . Authors presented the method of biogeography and its modifications , as well as results of the comparative analysis of genetic , biogeographic and memetic algorithms . Experiments were carried out on certain benchmarks from the library TSPLIB .
Original biogeographical and memetic algorithms for solving trans computational optimization problem are presented for the traveling salesman problem . Authors presented the method of biogeography and its modifications , as well as results of the comparative analysis of genetic , biogeographic and memetic algorithms . The authors conducted experimental verification of the effectiveness of the algorithms on known test functions .
The key challenge in DSPR is how to simultaneously keep track changes and search for the global optima . A multi-memory based multi-population memetic algorithm is proposed for DSPR in this paper . The proposed algorithm combines the strength of three different strategies , multi-memory , multi-population and memetic algorithm , aiming to effectively explore and exploit the search space .
Superiority of the new method is demonstrated by comparing it with three classification methods including Linear Discriminant Analysis , K-Nearest Neighbor and Support Vector Machine-Radial Basis Function . Self-Adaptive Differential Evolution , Differential Evolution/current-to-best/l , Particle Swarm Optimization and Genetic Algorithm-based clustering approaches have also been used here to study the relative performance of the proposed adaptive memetic algorithm-based clustering technique with respect to runtime and classification accuracy .
The supervised and unsupervised feature selection methods were investigated . To address the problems of optimizing the supervised feature selection methods so as to improve document clustering , memetic hybridization between filter and wrapper feature selection , known as Memetic Algorithm Feature Selection , was presented first . In order to deal with the unlabelled features , unsupervised feature selection method was also proposed .
A Review and Progress Report . Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search ( LS ) is coadapted alongside candidate solutions within a hybrid evolutionary system . Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems .
To prove this theorem we show that any base $ \alpha$ , dimension l code word will cycle in O(lq ) iterations of this Gray code mapping , and that this upper bound is attained . This theorem is a generalization of a theorem proven by Culberson [ Evolutionary Comput . , 2 ( 1995 ) , pp . 279 - -311 ] for the binary case .
A parallel solution to the implementation of evolutionary algorithms is proposed , where the most costly part of the whole evolutionary algorithm computations ( the population evaluation ) , is deported to a GPGPU card . Experiments are presented for two benchmark examples on two models of GPGPU cards : first a " toy " problem is used to illustrate some noticable behaviour characteristics before a real problem is tested out .
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems Differential evolution with improved elite archive mutation and dynamic parameter adjustment A hybrid rice optimization algorithm Optimizing the positive Lyapunov exponent in multi-scroll chaotic oscillators with differential evolution algorithm Performance of a hybrid EA-DE-memetic algorithm on CEC 2011 real world optimization problems An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem An Isoline Genetic Algorithm Advanced discussion mechanism-based brain storm optimization algorithm Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction A comparison of fitness-case sampling methods for genetic programming Genetic Programming in Data Modelling Anticipation versus adaptation in Evolutionary Algorithms: The case of Non-Stationary Clustering Listening to the noise: random fluctuations reveal gene network parameters Constructing quantitative models from qualitative mutant phenotypes: preferences in selecting sensory organ precursors Functional Localization of Genetic Network Programming Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling Mining equalized association rules from multi concept layers of ontology using Genetic Network Programming A genetic network programming based method to mine generalized association rules with ontology Evolutionary Reconstruction: Image Restoration for Space Variant Degradation Global optimization with derivative-free, derivative-based and evolutionary algorithms Variable Dependence Interaction And Multi-objective Optimisation Adaptive evolutionary planner/navigator for mobile robots Evolution of path finding Evolutionary optimization of Risk Budgeted long-short portfolios Using multivariate quantitative genetics theory to assist in EA customization Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem EDAS (EGNOS Data Access Service) Differential GPS Corrections: A Reliable Free-of-Charge Alternative for Precision Farming in Europe Evolutionary algorithms and elliptical copulas applied to continuous optimization problems Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms EDAS Method for Extended Hesitant Fuzzy Linguistic Multi-criteria Decision Making Managing security trade-offs in the Internet of Things using adaptive security EDAS: An Evaluation Prototype for Autonomic Event-Driven Adaptive Security in the Internet of Things Kernels of Mallows Models for Solving Permutation-based Problems Extending distance-based ranking models in estimation of distribution algorithms Fuzzy-EDAS (Evaluation Based on Distance from Average Solution) for Material Selection Problems Group Decision Analysis Algorithms with EDAS for Interval Fuzzy Sets EDAS METHOD FOR MULTIPLE CRITERIA GROUP DECISION MAKING WITH PICTURE FUZZY INFORMATION AND ITS APPLICATION TO GREEN SUPPLIERS SELECTIONS Memetic programming with adaptive local search using tree data structures Prime number generation using memetic programming A novel memetic genetic algorithm for solving traveling salesman problem based on multi-parent crossover technique An on-line variable-fidelity surrogate-assisted harmony search algorithm with multi-level screening strategy for expensive engineering design optimization A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems Block-insertion-based algorithms for the linear ordering problem A bee foraging-based memetic Harmony Search method A memetic-inspired harmony search method in optimal wind generator design Metaheuristics memes and biogeography for transcomputational combinatorial optimization problems Effectiveness Evaluation of Memetics and Biogeography Algorithms Using Benchmark and Trans Computational Tasks of Combinatorial Optimization A Multi-memory Multi-population Memetic Algorithm for Dynamic Shortest Path Routing in Mobile Ad-hoc Networks Differential evolution with temporal difference Q-learning based feature selection for motor imagery EEG data Document clustering with optimized unsupervised feature selection and centroid allocation Coevolving Memetic Algorithms: A Review and Progress Report Iterating an alpha-ary Gray Code Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards
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ICCSE IEEE Congress on Evolutionary Computation Advances in Metaheuristics for Hard Optimization IEEE Congress on Evolutionary Computation Genetic Systems Programming WSKS (2) IEEE Congress on Evolutionary Computation SMC GECCO CIFEr FOGA ICANNGA (1) IEEE Congress on Evolutionary Computation ICITST GECCO IEEE Congress on Evolutionary Computation CSTST SMC PRICAI CCMB Euro-Par
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