A real-coded genetic algorithm using the unimodal normal distribution crossover
作者： Isao Ono , Hajime Kita , Shigenobu Kobayashi
DOI: 10.1007/978-3-642-18965-4_8
关键词: Machine learning 、 Mutation (genetic algorithm) 、 Benchmark (computing) 、 Mathematics 、 Robustness (computer science) 、 Normal distribution 、 Epistasis 、 Distribution (mathematics) 、 Genetic algorithm 、 Mathematical optimization 、 Crossover 、 Artificial intelligence
摘要: This chapter presents a real-coded genetic algorithm using the Unimodal Normal Distribution Crossover (UNDX) that can efficiently optimize functions with epistasis among parameters. Most conventional crossover operators for function optimization have been reported to serious problem in their performance deteriorates considerably when they are applied We believe reason poor of is cannot keep distribution individuals unchanged process repetitive operations on In considering above problem, we introduce three guidelines, 'Preservation Statistics', 'Diversity Offspring', and 'Enhancement Robustness', designing show good even epistatic functions. UNDX meets guidelines very well by theoretical analysis shows better than some applying them benchmark including multimodal ones. also discuss improvements under relation between algorithms evolution strategies (ESs) correlated mutation.
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acm.org 参考文章(30)
Cezary Z. Janikow, Zbigniew Michalewicz, An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms. international conference on genetic algorithms. pp. 31- 36 ,(1991)
Nicholas J. Radcliffe, Forma Analysis and Random Respectful Recombination. Proc.4th Int'l Conf.on Genetic Algorithms. pp. 222- 229 ,(1991)
Shigenobu Kobayashi, Isao Ono, A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover. Proc. 7th ICGA, 1997. pp. 246- 253 ,(1997)
Masayuki Yamamura, Takahide Higuchi, Shigeyoshi Tsutsui, Multi-parent recombination with simplex crossover in real coded genetic algorithms genetic and evolutionary computation conference. pp. 657- 664 ,(1999)
Ralf Salomon, Performance Degradation of Genetic Algorithms Under Coordinate Rotation Evolutionary Programming. pp. 155- 161 ,(1996)
Zbigniew Michalewicz, Genetic Algorithms Plus Data Structures Equals Evolution Programs Springer-Verlag New York, Inc.. ,(1994)
H. Satoh, Minimal generation gap model for GAs considering both exploration and exploitation soft computing. pp. 494- 497 ,(1996)
Thomas Bäck, Hans-Paul Schwefel, Frank Hoffmeister, A Survey of Evolution Strategies. ICGA. pp. 2- 9 ,(1991)
Hans-Georg Beyer, Kalyanmoy Deb, Self-adaptation in real-parameter genetic algorithms with simulated binary crossover genetic and evolutionary computation conference. pp. 172- 179 ,(1999)
Kalyanmoy Deb, Ram Bhushan Agrawal, Simulated Binary Crossover for Continuous Search Space. Complex Systems. ,vol. 9, ,(1995)