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» Adapting binary fitness functions in Genetic Algorithms
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GECCO
2004
Springer
119views Optimization» more  GECCO 2004»
15 years 3 months ago
Topological Interpretation of Crossover
Abstract. In this paper we give a representation-independent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this defi...
Alberto Moraglio, Riccardo Poli
GECCO
2006
Springer
282views Optimization» more  GECCO 2006»
15 years 1 months ago
A genetic algorithm for the longest common subsequence problem
A genetic algorithm for the longest common subsequence problem encodes candidate sequences as binary strings that indicate subsequences of the shortest or first string. Its fitnes...
Brenda Hinkemeyer, Bryant A. Julstrom
CEC
2008
IEEE
15 years 4 months ago
A contour method in population-based stochastic algorithms
—Inspired by the contours in topography, this paper proposes a contour method for the population-based stochastic algorithms to solve the problems with continuous variables. Rely...
Ying Lin, Jun Zhang, Lu-kai Lan
SAC
2006
ACM
15 years 3 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
GECCO
2008
Springer
144views Optimization» more  GECCO 2008»
14 years 10 months ago
Maintaining diversity through adaptive selection, crossover and mutation
This paper presents an Adaptive Genetic Algorithm (AGA) where selection pressure, crossover and mutation probabilities are adapted according to population diversity statistics. Th...
Brian McGinley, Fearghal Morgan, Colm O'Riordan