Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
A crossover for complex building blocks overlapping
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Linkage identification by fitness difference clustering
Evolutionary Computation
Linkage identification based on epistasis measures to realize efficient genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Efficient linkage discovery by limited probing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A parallel genetic algorithm based on linkage identification
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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This paper empirically investigates parallel competent genetic algorithms (cGAs) [4]. cGAs, such as BOA [21], LINCGA [15], D5-GA [28], can solve GA-difficult problems by automatically learning problem structure as gene linkage. Parallel implementation of cGAs can reduce computational cost due to the linkage learning and give us problem solving environments for a wide spectrum of real-world problems. Although some parallel cGAs have been proposed [16, 18, 19], the effect of the parallelizations has not been investigated enough. This paper empirically discusses the applicability and property of parallel cGAs, including a new parallel cGA, parallel D5-GA.