Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization

2003 ◽  
pp. 1160-1171 ◽  
Author(s):  
Kwong-Sak Leung ◽  
Yong Liang
Keyword(s):  
Genetic Algorithm ◽  
Population Based ◽  
Function Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

A Species Conserving Genetic Algorithm for Multimodal Function Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 207-234 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiple Solutions ◽  
Species Conservation ◽  
Function Optimization ◽  
Test Problems ◽  
Next Generation ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
A New Technique

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

Erratum: A Species Conserving Genetic Algorithm for Multimodal Function Optimization

2003 ◽  
Vol 11 (1) ◽  
pp. 107-109 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Function Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function
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