Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Selection Schemes with Spatial Isolation for Genetic Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An analysis of Gray versus binary encoding in genetic search
Information Sciences: an International Journal - Special issue: Evolutionary computation
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
A binary encoding supporting both mutation and recombination
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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This paper describes enhancement to genetic algorithms that allows them to escape from the local optima during the optimization. The proposed method relies on the search space transformation that helps in resumption of the search process. It allows to continue the optimization for the same population's size without the random probing of the local optimum's neighborhood. It also reduces a necessity for the genetic algorithm's restart with a differently distributed initial population. In the result, it can converge to the global optimum after a lower number of iterations. The proposed method is applicable to optimization problems described by any number of real---valued variables. The paper describes this method and presents results from the experimental evaluation that highlights properties of the proposed method.