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» Group Properties of Crossover and Mutation
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ICML
2003
IEEE
13 years 11 months ago
Evolutionary MCMC Sampling and Optimization in Discrete Spaces
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
Malcolm J. A. Strens
GECCO
2005
Springer
200views Optimization» more  GECCO 2005»
13 years 11 months ago
An extension of vose's markov chain model for genetic algorithms
The paper presents an extension of Vose’s Markov chain model for genetic algorithm (GA). The model contains not only standard genetic operators such as mutation and crossover bu...
Anna Paszynska
GECCO
2009
Springer
122views Optimization» more  GECCO 2009»
14 years 25 days ago
Evolving symmetric and modular neural networks for distributed control
Problems such as the design of distributed controllers are characterized by modularity and symmetry. However, the symmetries useful for solving them are often difficult to determ...
Vinod K. Valsalam, Risto Miikkulainen
EC
2006
163views ECommerce» more  EC 2006»
13 years 6 months ago
An Extension of Geiringer's Theorem for a Wide Class of Evolutionary Search Algorithms
The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. Th...
Boris Mitavskiy, Jonathan E. Rowe
ICPPW
2005
IEEE
13 years 12 months ago
Gene Ordering in Microarray Data Using Parallel Memetic Algorithms
This paper addresses the Microarray Gene Ordering problem. It consists in ordering a set of genes, grouping together the ones with similar behavior. This behavior can be measured ...
Alexandre Mendes, Carlos Cotta, Vinícius Ga...