Yong-Qing Huang

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The improvement of the algorithm's performance and the reduction of a use's fatigue are important issues in interactive genetic algorithms (IGAs). In order to achieve these purposes, the idea of submitting users the most satisfactory individuals estimated directly is put forward. Firstly, three issues about samples that is a determinative factors of the(More)
Learning is the core of intelligence algorithm. Genetic algorithm (GA), as an intelligent algorithm, has its own learning mechanism. This paper focuses on the learning matrix of evolutionary operators in GA. From the viewpoint of solution generation, the learning mechanism in GA is studied and the matrix expression of recombination and mutation is given. A(More)
There are two kinds of methods to determine parameters in GAs: online and offline. This paper studied the offline determinations of parameters from the decision space but not fitness landscape. In order to make full use of operators’ ability to explore/exploit the subspace, the population size and terminal generation number should satisfy two conditions:(More)
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