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» Genetic Algorithms for Dynamic Test Data Generation
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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...
GECCO
2005
Springer
207views Optimization» more  GECCO 2005»
14 years 1 months ago
Adaptive crossover and mutation in genetic algorithms based on clustering technique
Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By...
Jun Zhang, Henry Shu-Hung Chung, Jinghui Zhong
GECCO
2005
Springer
232views Optimization» more  GECCO 2005»
14 years 1 months ago
Factorial representations to generate arbitrary search distributions
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...
Marc Toussaint
IAW
2003
IEEE
14 years 1 months ago
An Evolutionary Approach to Generate Fuzzy Anomaly Signatures
Abstract— This paper describes the generation of fuzzy signatures to detect some cyber attacks. This approach is an enhancement to our previous work, which was based on the princ...
Fabio A. González, Jonatan Gómez, Ma...
GECCO
2006
Springer
165views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolving musical performance profiles using genetic algorithms with structural fitness
This paper presents a system that uses Genetic Algorithm (GA) to evolve hierarchical pulse sets (i.e., hierarchical duration vs. amplitude matrices) for expressive music performan...
Qijun Zhang, Eduardo Reck Miranda