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CIMCA
2005
IEEE
14 years 1 months ago
Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms
This paper presents the comparison of performance on a simple genetic algorithm (SGA) using roulette wheel selection and tournament selection. A SGA is mainly composed of three ge...
Jinghui Zhong, Xiaomin Hu, Jun Zhang, Min Gu
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
14 years 1 months ago
Combining competent crossover and mutation operators: a probabilistic model building approach
This paper presents an approach to combine competent crossover and mutation operators via probabilistic model building. Both operators are based on the probabilistic model buildin...
Cláudio F. Lima, Kumara Sastry, David E. Go...
AUSAI
2005
Springer
14 years 1 months ago
Accelerating Real-Valued Genetic Algorithms Using Mutation-with-Momentum
: In a canonical genetic algorithm, the reproduction operators (crossover and mutation) are random in nature. The direction of the search carried out by the GA system is driven pur...
Luke Temby, Peter Vamplew, Adam Berry
SAC
2006
ACM
14 years 1 months ago
Adaptive mutation with fitness and allele distribution correlation for genetic algorithms
In this paper, a new gene based adaptive mutation scheme is proposed for genetic algorithms (GAs), where the information on gene based fitness statistics and on gene based allele...
Shengxiang Yang, Sima Uyar
NCI
2003
107views Neural Networks» more  NCI 2003»
13 years 8 months ago
Sexual Selection with Competitive/Co-operative Operators for Genetic Algorithms
In a standard genetic algorithm (GA), individuals reproduce asexually: any two organisms may be parents in crossover. Gender separation and sexual selection here inspire a model o...
Jose Sánchez-Velazco, John A. Bullinaria