Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Proceedings of the 5th International Conference on Genetic Algorithms
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Combining competent crossover and mutation operators: a probabilistic model building approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Sporadic model building for efficiency enhancement of hierarchical BOA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
Copula estimation of distribution algorithms based on exchangeable Archimedean copula
International Journal of Computer Applications in Technology
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In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from O(n3) to O(n2), speeding up the algorithm by 1000 times on a 4096 bits problem. Then, a local-search hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results are the first steps toward a competent and efficient Genetic Algorithm.