Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Widely convergent method for finding multiple solutions of simultaneous nonlinear equations
IBM Journal of Research and Development
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Introducing intervention targeting into estimation of distribution algorithms
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Hybrid metaheuristics have received considerable interest in recent years. Since several years ago, a wide variety of hybrid approaches have been proposed in the literature including the new GA-EDA approach. We have design and implemented an extension to this GA-EDA approach, based on statistical significance tests. This approach had allowed us to make an study of the balance of diversification (exploration) and intensification (exploitation) in Genetic Algorithms and Estimation of Distribution Algorithms.