A connectionist machine for genetic hillclimbing
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An adaptive crossover distribution mechanism for genetic algorithms
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Adaptation in natural and artificial systems
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Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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A Survey of Optimization by Building and Using Probabilistic Models
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On Appropriate Adaptation Levels for the Learning of Gene Linkage
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Proceedings of the 6th International Conference on Genetic Algorithms
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PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Linkage Problem, Distribution Estimation, and Bayesian Networks
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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
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Evolutionary Computation
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Evolutionary Computation
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GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The benefits of computing with introns
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Tightness time for the linkage learning genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
IEEE Transactions on Evolutionary Computation
Dependency structure matrix, genetic algorithms, and effective recombination
Evolutionary Computation
Improving the scalability of EA techniques: a case study in clustering
EA'09 Proceedings of the 9th international conference on Artificial evolution
Sensibility of linkage information and effectiveness of estimated distributions
Evolutionary Computation
A surrogate-assisted linkage inference approach in genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Interaction detection for hybrid decomposable problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.