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» An Analysis of Multi-Point Crossover
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TSMC
2002
93views more  TSMC 2002»
13 years 7 months ago
Statistical analysis of the main parameters involved in the design of a genetic algorithm
Abstract--Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover,...
Ignacio Rojas, Jesús González, H&eac...
CPAIOR
2008
Springer
13 years 9 months ago
Fitness-Distance Correlation and Solution-Guided Multi-point Constructive Search for CSPs
Solution-Guided Multi-Point Constructive Search (SGMPCS) is a complete, constructive search technique that has been shown to out-perform standard constructive search techniques on ...
Ivan Heckman, J. Christopher Beck
CEC
2005
IEEE
14 years 1 months ago
Theoretical analysis of generalised recombination
In this paper we propose, model theoretically and study a general notion of recombination for fixed-length strings where homologous crossover, inversion, gene duplication, gene d...
Riccardo Poli, Christopher R. Stephens
GECCO
2008
Springer
126views Optimization» more  GECCO 2008»
13 years 8 months ago
The impact of population size on code growth in GP: analysis and empirical validation
The crossover bias theory for bloat [18] is a recent result which predicts that bloat is caused by the sampling of short, unfit programs. This theory is clear and simple, but it ...
Riccardo Poli, Nicholas Freitag McPhee, Leonardo V...
FOGA
1998
13 years 9 months ago
Understanding Interactions among Genetic Algorithm Parameters
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been tr...
Kalyanmoy Deb, Samir Agrawal