The Experts below are selected from a list of 19728 Experts worldwide ranked by ideXlab platform
Ender Ozcan - One of the best experts on this subject based on the ideXlab platform.
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a step size based self adaptive Mutation Operator for evolutionary programming
Genetic and Evolutionary Computation Conference, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
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GECCO (Companion) - A step size based self-adaptive Mutation Operator for evolutionary programming
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
Libin Hong - One of the best experts on this subject based on the ideXlab platform.
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a step size based self adaptive Mutation Operator for evolutionary programming
Genetic and Evolutionary Computation Conference, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
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GECCO (Companion) - A step size based self-adaptive Mutation Operator for evolutionary programming
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
Liu Yang - One of the best experts on this subject based on the ideXlab platform.
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particle swarm optimization algorithm with Mutation Operator for particle filter noise reduction in mechanical fault diagnosis
International Journal of Pattern Recognition and Artificial Intelligence, 2020Co-Authors: Hanxin Chen, Liu Yang, Dong Liang Fan, Lu Fang, Wenjian Huang, Jinmin Huang, Chenghao Cao, Li ZengAbstract:In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with Mutation Operator, and...
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a distribution network reconfiguration algorithm based on evolutionary programming Mutation Operator
Power system technology, 2012Co-Authors: Liu YangAbstract:In allusion to the problems of slow evolution and hard to reposefully converge which commonly exist in evolutionary programming algorithm and considering the two objects of minimum loss and load equalization,a new Mutation Operator for evolutionary programming is proposed.To ensure that the new individuals due to the Mutation can draw close to optimal individual actively,on the one hand the topological restructure times of the Mutation Operator should be in inverse proportion to the generations of evolution,on the other hand the selection Operator for switches to be switched on and that for switches to be switched off should be added during the topological restructure process.Meanwhile,to speed up the calculation of the whole distribution network reconfiguration algorithm,a simplified method to calculate power loss,quadratic load moment,selection Operator for switches to be switched on and that for switches to be switched off is researched.The feasibility of the proposed method is verified by operation optimization and auxiliary decision-making system for an actual 10 kV distribution network in Henan province,China.
Asier Perallos - One of the best experts on this subject based on the ideXlab platform.
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short communication comments on albayrak m allahverdy n 2011 development a new Mutation Operator to solve the traveling salesman problem by aid of genetic algorithms expert systems with applications 38 3 1313 1320 a proposal of good practice
Expert Systems With Applications, 2014Co-Authors: Eneko Osaba, Enrique Onieva, Fernando Diaz, Roberto Carballedo, Asier PerallosAbstract:This short note presents a discussion arisen after reading ''Development a new Mutation Operator to solve the Traveling Salesman Problem by aid of genetic algorithms'', by Murat Albayrak and Novruz Allahverdi, (2011). Expert System with Applications (38) (pp. 1313-1320). The discussed paper presents a new greedy Mutation Operator to solve the well-known Traveling Salesman Problem. To prove the quality of their new Operator, the authors compare different versions of a classical genetic algorithm, each of one with a different Mutation Operator. The experimentation shown by the authors can generate some controversy. In this short note, we explain the origin of this controversy and we bring a solution to prevent it in future publications.
John H Drake - One of the best experts on this subject based on the ideXlab platform.
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a step size based self adaptive Mutation Operator for evolutionary programming
Genetic and Evolutionary Computation Conference, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
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GECCO (Companion) - A step size based self-adaptive Mutation Operator for evolutionary programming
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014Co-Authors: Libin Hong, John H Drake, Ender OzcanAbstract:The Mutation Operator is the only genetic Operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Levy distributions as Mutation Operators. According to the No Free Lunch theorem [9], no single Mutation Operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive Mutation Operator for Evolutionary Programming (SSEP). In SSEP, the Mutation Operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate Mutation Operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static Mutation Operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple Mutation Operators.
