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Chaos Genetic Algorithm and its Application in Function Optimization

Pla Electronic

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Abstract

By combining chaos optimization and genetic algorithm, the Chaos Genetic Algorithm (CGA) is presented, which is applied in solving the function optimization problem. The chaos optimization is introduced in different phase of population evolution, which improves the total performance of genetic algorithm greatly. The experiment results show that CGA can get global optimum solution more efficiently and has higher convergence rate compared with Standard Genetic Algorithm (SGA).

About this research paper

What this paper is about

By combining chaos optimization and genetic algorithm, the Chaos Genetic Algorithm (CGA) is presented, which is applied in solving the function optimization problem. The chaos optimization is introduced in different phase of population evolution, which improves the total performance of genetic algorithm greatly. The experiment results show that CGA can get global optimum solution more efficiently and has higher convergence rate compared with Standard Genetic Algorithm (SGA).

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

By combining chaos optimization and genetic algorithm, the Chaos Genetic Algorithm (CGA) is presented, which is applied in solving the function optimization problem. The chaos optimization is introduced in different phase of population evolution, which improves the total performance of genetic algorithm greatly. The experiment results show that CGA can get global optimum solution more efficiently and has higher convergence rate compared with Standard Genetic Algorithm (SGA).

Key concepts: Computer science, CHAOS (operating system), Meta-optimization, Genetic algorithm, Population-based incremental learning, Mathematical optimization, Convergence (economics), Function optimization

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