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2003•Journal of Hefei University of TechnologyRequires access

An improved genetic algorithm-The disturbance genetic algorithm

Yong Liu

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Abstract

In order to solve the problem of low precision and premature convergence of standard genetic algorithm which is based on binary number coding, an improved genetic algorithm called disturbance genetic algorithm (DGA) is presented,and the searching capability of the algorithm is improved by disturbing the search zone slightly. With the improved algorithm,the multiple hump function can be dealt with efficiently and the goal of global convergence achieved. The design and structure of the improved genetic algorithm are discussed in this paper. The effectiveness of the improved genetic algorithm is also analyzed.

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What this paper is about

In order to solve the problem of low precision and premature convergence of standard genetic algorithm which is based on binary number coding, an improved genetic algorithm called disturbance genetic algorithm (DGA) is presented,and the searching capability of the algorithm is improved by disturbing the search zone slightly. With the improved algorithm,the multiple hump function can be dealt with efficiently and the goal of global convergence achieved. The design and structure of the improved genetic algorithm are discussed in this paper. The effectiveness of the improved genetic algorithm is also analyzed.

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Available abstract

In order to solve the problem of low precision and premature convergence of standard genetic algorithm which is based on binary number coding, an improved genetic algorithm called disturbance genetic algorithm (DGA) is presented,and the searching capability of the algorithm is improved by disturbing the search zone slightly. With the improved algorithm,the multiple hump function can be dealt with efficiently and the goal of global convergence achieved. The design and structure of the improved genetic algorithm are discussed in this paper. The effectiveness of the improved genetic algorithm is also analyzed.

Key concepts: Population-based incremental learning, Genetic algorithm, Algorithm, Cultural algorithm, Meta-optimization, Coding (social sciences), Convergence (economics), Premature convergence

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