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» A New Crossover Operator for Genetic Algorithms
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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
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
GECCO
2004
Springer
106views Optimization» more  GECCO 2004»
14 years 1 months ago
Mutation Rates in the Context of Hybrid Genetic Algorithms
Traditionally, the mutation rates of genetic algorithms are fixed or decrease over the generations. Although it seems to be reasonable for classical genetic algorithms, it may not...
Seung-Hee Bae, Byung Ro Moon
GECCO
2005
Springer
14 years 1 months ago
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic al...
Yong-Hyuk Kim, Byung Ro Moon
GECCO
2000
Springer
109views Optimization» more  GECCO 2000»
13 years 11 months ago
Crossover in Probability Spaces
This paper proposes a new crossover operator for searching over discrete probability spaces. The design of the operator is considered in the light of recent theoretical insights i...
Siddhartha Bhattacharyya, Marvin D. Troutt