The Experts below are selected from a list of 1740 Experts worldwide ranked by ideXlab platform
O.g. Kakde - One of the best experts on this subject based on the ideXlab platform.
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IICAI - Real-Code Self-Adaptive Genetic Algorithm
2020Co-Authors: Mukesh M. Raghuwanshi, O.g. KakdeAbstract:Genetic algorithm (GA) implements the idea of evolution; it is natural to expect adaptation to be used not only for finding solutions to a problem, but also tuning the algorithm for the particular problem. The purpose of dynamic Operator adaptation is to exploit information gained, either implicitly or explicitly. This paper presents real-coded self-adaptive GA (RAGA), which is a robust steady-state GA. It uses two multi-parent parent- centric Recombination Operators: multi-parent Recombination Operator with polynomial distribution (MPX) and multi-parent Recombination Operator with lognormal distribution (MLX). We introduce a mechanism of adapting Operator probabilities according the landscapes of a given function. The probability of Operator used for Recombination operation depends upon its ability to produce good offspring. Replacement plan rewards the Operator that produces a good offspring. Performance of RAGA is investigated on commonly used unimodal and multi-modal test functions.
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Distributed Quasi Steady-State Genetic Algorithm with Niches and Species
International Journal of Computational Intelligence Research, 2020Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:In this paper, we have proposed a new real coded genetic algorithm with species and sexual selection (GAS3). GAS3 is a distributed quasi steady-state real-coded genetic algorithm. GAS3 uses sex determination method (SDM) to determine the sex (male or female) of members in population. Each female member is considered as a niche in population and the species formation takes place around these niches. Sexual selection strategy selects female and required number of male members from the species to perform the Recombination operation. The Parent-centric Recombination Operators are used to generate offspring. If species is not performing well, then the merging to the nearby species takes place. Explorative Recombination Operator is used to explore a wide range of search space in the beginning, while exploitative Recombination Operator is used in the later stages. The performance of GAS3 is tested on unimodal and multi-modal test functions. It got success in solving wide range of problems. Its performance is also compared with the other real-coded genetic algorithms.
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IEEE Congress on Evolutionary Computation - Study of Operator’s adaptability and scale-up study for RAGA
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The studies have shown that a class of Recombination Operators is more suitable to tackle certain problems than others. It is observed that the multi-parent Recombination Operator with polynomial distribution (MPX) is exploitative and the multi-parent Recombination Operator with lognormal distribution (MLX) is explorative, in nature. Use of productive Operators is necessary for a genetic algorithm to uncover new fitter points in the search space to improve its overall performance. Real-coded self-Adaptive GA (RAGA) uses two multi-parent Recombination Operators (MPX and MLX). The use of particular Operator to generate offspring during evolution process depends on its ability to produce good offspring. This paper presents the effect of Operator's adaptability in solving test problems. Also scale-up study analyses the performance of RAGA with increasing number of control variables.
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Study of Operator’s adaptability and scale-up study for RAGA
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The studies have shown that a class of Recombination Operators is more suitable to tackle certain problems than others. It is observed that the multi-parent Recombination Operator with polynomial distribution (MPX) is exploitative and the multi-parent Recombination Operator with lognormal distribution (MLX) is explorative, in nature. Use of productive Operators is necessary for a genetic algorithm to uncover new fitter points in the search space to improve its overall performance. Real-coded self-Adaptive GA (RAGA) uses two multi-parent Recombination Operators (MPX and MLX). The use of particular Operator to generate offspring during evolution process depends on its ability to produce good offspring. This paper presents the effect of Operator's adaptability in solving test problems. Also scale-up study analyses the performance of RAGA with increasing number of control variables.
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probability distribution based Recombination Operator to solve unimodal and multi modal problems
International Journal of Knowledge-based and Intelligent Engineering Systems, 2006Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The neighborhood-based crossover Operators used in real coded genetic algorithm (RCGA) are based on some probability distribution. It is observed that each crossover Operator directs the search towards a different zone in the neighborhood of the parents. The quality of the elements that belong to the visited region depends on the particular problems to be solved. Different crossover Operators perform differently with respect to the problems, even at the different stages of the genetic process in the same problem. In this paper, the role of probability distribution is empirically investigated on unimodal and multi-modal test problems. It is observed that the Operator based on polynomial distribution achieves superior performance on unimodal test problems. The lognormal distribution based Operator is efficient in solving multi-modal problems.
M M Raghuwanshi - One of the best experts on this subject based on the ideXlab platform.
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Distributed Quasi Steady-State Genetic Algorithm with Niches and Species
International Journal of Computational Intelligence Research, 2020Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:In this paper, we have proposed a new real coded genetic algorithm with species and sexual selection (GAS3). GAS3 is a distributed quasi steady-state real-coded genetic algorithm. GAS3 uses sex determination method (SDM) to determine the sex (male or female) of members in population. Each female member is considered as a niche in population and the species formation takes place around these niches. Sexual selection strategy selects female and required number of male members from the species to perform the Recombination operation. The Parent-centric Recombination Operators are used to generate offspring. If species is not performing well, then the merging to the nearby species takes place. Explorative Recombination Operator is used to explore a wide range of search space in the beginning, while exploitative Recombination Operator is used in the later stages. The performance of GAS3 is tested on unimodal and multi-modal test functions. It got success in solving wide range of problems. Its performance is also compared with the other real-coded genetic algorithms.
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Review on Real Coded Genetic Algorithms Used in Multiobjective Optimization
2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010Co-Authors: Rahila Patel, M M RaghuwanshiAbstract:This paper gives a short review of real coded genetic algorithm (RCGA) used for multiobjective optimization. Handling of continues search space is very easy with RCGA and solution representation is very close to natural formulation of real-world problems. Because of the obvious reasons, most of real-world multi-objective optimization problems are solved using RCGA. The topics discussed in this paper include new algorithms, design issues of multi-objective optimization like efficiency, scalability, constraint handling and self-adaptation. This discussion suggests potential areas for future research, namely, design of new algorithm, new Recombination Operator and Pareto optimal front formation techniques.
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IEEE Congress on Evolutionary Computation - Study of Operator’s adaptability and scale-up study for RAGA
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The studies have shown that a class of Recombination Operators is more suitable to tackle certain problems than others. It is observed that the multi-parent Recombination Operator with polynomial distribution (MPX) is exploitative and the multi-parent Recombination Operator with lognormal distribution (MLX) is explorative, in nature. Use of productive Operators is necessary for a genetic algorithm to uncover new fitter points in the search space to improve its overall performance. Real-coded self-Adaptive GA (RAGA) uses two multi-parent Recombination Operators (MPX and MLX). The use of particular Operator to generate offspring during evolution process depends on its ability to produce good offspring. This paper presents the effect of Operator's adaptability in solving test problems. Also scale-up study analyses the performance of RAGA with increasing number of control variables.
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Study of Operator’s adaptability and scale-up study for RAGA
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The studies have shown that a class of Recombination Operators is more suitable to tackle certain problems than others. It is observed that the multi-parent Recombination Operator with polynomial distribution (MPX) is exploitative and the multi-parent Recombination Operator with lognormal distribution (MLX) is explorative, in nature. Use of productive Operators is necessary for a genetic algorithm to uncover new fitter points in the search space to improve its overall performance. Real-coded self-Adaptive GA (RAGA) uses two multi-parent Recombination Operators (MPX and MLX). The use of particular Operator to generate offspring during evolution process depends on its ability to produce good offspring. This paper presents the effect of Operator's adaptability in solving test problems. Also scale-up study analyses the performance of RAGA with increasing number of control variables.
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probability distribution based Recombination Operator to solve unimodal and multi modal problems
International Journal of Knowledge-based and Intelligent Engineering Systems, 2006Co-Authors: M M Raghuwanshi, O.g. KakdeAbstract:The neighborhood-based crossover Operators used in real coded genetic algorithm (RCGA) are based on some probability distribution. It is observed that each crossover Operator directs the search towards a different zone in the neighborhood of the parents. The quality of the elements that belong to the visited region depends on the particular problems to be solved. Different crossover Operators perform differently with respect to the problems, even at the different stages of the genetic process in the same problem. In this paper, the role of probability distribution is empirically investigated on unimodal and multi-modal test problems. It is observed that the Operator based on polynomial distribution achieves superior performance on unimodal test problems. The lognormal distribution based Operator is efficient in solving multi-modal problems.
Marin Golub - One of the best experts on this subject based on the ideXlab platform.
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On the Recombination Operator in the real-coded genetic algorithms
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Stjepan Picek, Domagoj Jakobovic, Marin GolubAbstract:Crossover is the most important Operator in real-coded genetic algorithms. However, the choice of the best Operator for a specific problem can be a difficult task. In this paper we compare 16 crossover Operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing Operators. The results show that there are significant differences in efficiency of different crossover Operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover Operators yields the best results.
Ramin Halavati - One of the best experts on this subject based on the ideXlab platform.
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SYMBIOTIC EVOLUTION OF RULE BASED CLASSIFIER SYSTEMS
International Journal on Artificial Intelligence Tools, 2009Co-Authors: Ramin Halavati, Saeed Bagheri Shouraki, Sima Lotfi, Pooya EsfandiarAbstract:Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a Recombination Operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design Recombination Operator, this paper presents a novel algorithm for rule base evolution based on natural process of symbiogenesis. The algorithm uses symbiotic combination Operator instead of traditional sexual Recombination Operator of genetic algorithms. This Operator takes two chromosomes with different number of genes (rules here) and merges them by combining all the information content of both chromosomes. Using this Operator results in two major advantages: First, it totally removes the need to design the Recombination Operator and therefore is easier to use; second, it outperforms traditional genetic algorithm both in emergence speed and classification rate, this is tested and presented on some globally used benchmarks.
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HIS - Symbiotic Tabu Search, A General Evolutionary Optimization Approach
7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007Co-Authors: Ramin Halavati, Saeed Bagheri Shouraki, Bahareh Jafari Jashmi, Mojdeh Jalali HeraviAbstract:Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations - hopefully producing an offspring that has the good characteristics of both parents. Symbiotic Combination is formerly introduced as an alternative for sexual Recombination Operator to overcome the need of explicit design of Recombination Operators in GA. This paper presents an optimization algorithm based on using this Operator in Tabu Search. The algorithm is benchmarked on two problem sets and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing success rates higher than both cited algorithms.
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GECCO - Symbiotic tabu search
Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07, 2007Co-Authors: Ramin Halavati, Saeed Bagheri Shouraki, Bahareh Jafari Jashmi, Mojdeh Jalali HeraviAbstract:Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations hopefully producing an offspring that has the good characteristics of both parents. Symbiotic Combination is formerly introduced as an alternative for sexual Recombination Operator to overcome the need of explicit design of Recombination Operators in GA all. This paper presents an optimization algorithm based on using this Operator in Tabu Search. The algorithm is benchmarked on two problem sets and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing success rates higher than both cited algorithms.
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Symbiotic Tabu Search, A General Evolutionary Optimization Approach
7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007Co-Authors: Ramin Halavati, Saeed Bagheri Shouraki, Bahareh Jafari Jashmi, Mojdeh Jalali HeraviAbstract:Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations - hopefully producing an offspring that has the good characteristics of both parents. Symbiotic Combination is formerly introduced as an alternative for sexual Recombination Operator to overcome the need of explicit design of Recombination Operators in GA. This paper presents an optimization algorithm based on using this Operator in Tabu Search. The algorithm is benchmarked on two problem sets and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing success rates higher than both cited algorithms.
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A general purpose optimization approach
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Ramin Halavati, Saeed Bagheri Shouraki, Mojdeh Jalali Heravi, Bahareh Jafari JashmiAbstract:Recombination in the genetic algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations - hopefully producing an offspring that has the good characteristics of both parents and this requires explicit chromosome and Recombination Operator design. This paper presents a novel evolutionary approach based on symbiogenesis which uses symbiotic combination instead of sexual Recombination and using this Operator, it requires no domain knowledge for chromosome or combination Operator design. The algorithm is benchmarked on three problem sets, combinatorial optimization, deceptive, and fully deceptive, and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing higher success rates and faster results in compare with both cited algorithms.
Simon M. Lucas - One of the best experts on this subject based on the ideXlab platform.
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IEEE Congress on Evolutionary Computation - A statistically aligned Recombination Operator for finite state machines
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Simon M. LucasAbstract:Learning finite state machines from samples of data has been extensively studied within machine learning and since the dawn of evolutionary computation. Conventional crossover or Recombination Operators used for finite state machines suffer from the competing conventions problem, caused by the combinatorial number of isomorphisms of each distinct machine. This paper introduces an efficient alignment Operator to counteract this phenomenon. Results show that when in the neighbourhood of the target machine, the aligned crossover Operator reaches the optimum in far few steps (on average) than either a naive crossover Operator or a standard flip-style mutation Operator.
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A statistically aligned Recombination Operator for finite state machines
2007 IEEE Congress on Evolutionary Computation, 2007Co-Authors: Simon M. LucasAbstract:Learning finite state machines from samples of data has been extensively studied within machine learning and since the dawn of evolutionary computation. Conventional crossover or Recombination Operators used for finite state machines suffer from the competing conventions problem, caused by the combinatorial number of isomorphisms of each distinct machine. This paper introduces an efficient alignment Operator to counteract this phenomenon. Results show that when in the neighbourhood of the target machine, the aligned crossover Operator reaches the optimum in far few steps (on average) than either a naive crossover Operator or a standard flip-style mutation Operator.
