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Genetic Algorithm with Age Structure

Yan Yang

Open publisher page 1 citations

Abstract

In this paper,a novel genetic algorithm with age structure is proposed. Standard genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multi-model functions . To solve the problem,the genetic algorithm with age structure is presented referring natural laws and the method of genetic algorithms. 1This genetic algorithm can combat premature convergence and keep the diversity of population through different operation with different age units,and thereby converge conveniently on global solutions.

About this research paper

What this paper is about

In this paper,a novel genetic algorithm with age structure is proposed. Standard genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multi-model functions . To solve the problem,the genetic algorithm with age structure is presented referring natural laws and the method of genetic algorithms. 1This genetic algorithm can combat premature convergence and keep the diversity of population through different operation with different age units,and thereby converge conveniently on global solutions.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

In this paper,a novel genetic algorithm with age structure is proposed. Standard genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multi-model functions . To solve the problem,the genetic algorithm with age structure is presented referring natural laws and the method of genetic algorithms. 1This genetic algorithm can combat premature convergence and keep the diversity of population through different operation with different age units,and thereby converge conveniently on global solutions.

Key concepts: Premature convergence, Genetic algorithm, Convergence (economics), Cultural algorithm, Genetic representation, Population-based incremental learning, Mathematical optimization, Computer science

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