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» Evolving Crossover Operators for Function Optimization
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GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
13 years 12 months ago
Genetically programmed learning classifier system description and results
An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
Gregory Anthony Harrison, Eric W. Worden
GECCO
2007
Springer
183views Optimization» more  GECCO 2007»
13 years 12 months ago
Distribution replacement: how survival of the worst can out perform survival of the fittest
A new family of "Distribution Replacement” operators for use in steady state genetic algorithms is presented. Distribution replacement enforces the members of the populatio...
Howard Tripp, Phil Palmer
GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
13 years 6 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
GECCO
2004
Springer
140views Optimization» more  GECCO 2004»
13 years 11 months ago
Keeping the Diversity with Small Populations Using Logic-Based Genetic Programming
We present a new method of Logic-Based Genetic Programming (LBGP). Using the intrinsic mechanism of backtracking in Prolog, we utilize large individual programs with redundant clau...
Ken Taniguchi, Takao Terano
ECAL
2005
Springer
13 years 11 months ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari