What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
On evolutionary exploration and exploitation
Fundamenta Informaticae
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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Schemata evolution and building blocks
Evolutionary Computation
New entropy-based measures of gene significance and epistasis
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Chi-Square matrix: an approach for building-block identification
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
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This paper proposes a new algorithm to identify and compose building blocks based on minimum mutual information criterion. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks in population explicitly. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with Bayesian Optimization Algorithm, Hierarchical Bayesian Optimization Algorithm, and Chi-square Matrix. This proposed algorithm is simple, easy to tune and fast.