20102010 3rd International Conference on Biomedical Engineering and InformaticsRequires access

The enhanced genetic algorithms for the optimization design

Pengfei Guo, Xuezhi Wang, Yingshi Han

Open publisher page 267 citations

Abstract

Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.

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What this paper is about

Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.

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Available abstract

Three different kinds of the novel enhanced genetic algorithm procedures including the hybrid genetic algorithm, interval genetic algorithm and hybrid interval genetic algorithm are respectively presented. As the results of the proven systems show, the hybrid genetic algorithm can determines the better optimum design than the traditional optimization algorithms and genetic algorithm. The interval genetic algorithm and hybrid interval genetic algorithm can avoid calculating system slope in traditional interval analysis and determines the optimum interval range of the parameters under allowable corresponding objective error boundary. It is the first time that genetic algorithm has been applied to interval optimization process.

Key concepts: Genetic algorithm, Interval (graph theory), Meta-optimization, Population-based incremental learning, Cultural algorithm, Algorithm, Computer science, Range (aeronautics)

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