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» Generalized crowding for genetic algorithms
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GECCO
2006
Springer
205views Optimization» more  GECCO 2006»
15 years 1 months ago
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP
In contrast with the diverse array of genetic algorithms, the Genetic Programming (GP) paradigm is usually applied in a relatively uniform manner. Heuristics have developed over t...
L. Darrell Whitley, Marc D. Richards, J. Ross Beve...
GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
15 years 1 months ago
A new crossover technique for Cartesian genetic programming
Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped ...
Janet Clegg, James Alfred Walker, Julian Francis M...
GECCO
2008
Springer
174views Optimization» more  GECCO 2008»
14 years 10 months ago
Mask functions for the symbolic modeling of epistasis using genetic programming
The study of common, complex multifactorial diseases in genetic epidemiology is complicated by nonlinearity in the genotype-to-phenotype mapping relationship that is due, in part,...
Ryan J. Urbanowicz, Nate Barney, Bill C. White, Ja...
GECCO
1999
Springer
158views Optimization» more  GECCO 1999»
15 years 1 months ago
Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems
In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level o...
Hisashi Handa, Osamu Katai, Tadataka Konishi, Mits...
GECCO
2006
Springer
186views Optimization» more  GECCO 2006»
15 years 1 months ago
Characterizing large text corpora using a maximum variation sampling genetic algorithm
An enormous amount of information available via the Internet exists. Much of this data is in the form of text-based documents. These documents cover a variety of topics that are v...
Robert M. Patton, Thomas E. Potok