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GECCO
2008
Springer
144views Optimization» more  GECCO 2008»
13 years 6 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
FLAIRS
2006
13 years 6 months ago
Genetic Programming: Analysis of Optimal Mutation Rates in a Problem with Varying Difficulty
In this paper we test whether a correlation exists between the optimal mutation rate and problem difficulty. We find that the range of optimal mutation rates is inversely proporti...
Alan Piszcz, Terence Soule
GECCO
2007
Springer
155views Optimization» more  GECCO 2007»
13 years 11 months ago
Differential evolution and non-separability: using selective pressure to focus search
Recent results show that the Differential Evolution algorithm has significant difficulty on functions that are not linearly separable. On such functions, the algorithm must rely...
Andrew M. Sutton, Monte Lunacek, L. Darrell Whitle...
CORR
2006
Springer
94views Education» more  CORR 2006»
13 years 5 months ago
Fitness Uniform Optimization
Abstract-- In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. Th...
Marcus Hutter, Shane Legg
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
13 years 12 months ago
Three interconnected parameters for genetic algorithms
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
Pedro A. Diaz-Gomez, Dean F. Hougen