A Genetic Algorithm Inspired by the Neutral Theory and Its Application to the Formation of Ladder-Network

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  • ISOKAWA Teijiro
    Department of Computer Engineering, Faculty of Engineering, Himeji Institute of Technology
  • MATSUI Nobuyuki
    Department of Computer Engineering, Faculty of Engineering, Himeji Institute of Technology
  • NISHIMURA Haruhiko
    Studies of Information Science, Hyogo University of Education

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  • 中立説を考慮した遺伝的アルゴリズムとその梯子型ネットワーク形成への適用
  • チュウリツセツ オ コウリョ シタ イデンテキ アルゴリズム ト ソノ ハシゴガタ ネットワーク ケイセイ エ ノ テキヨウ

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Abstract

Genetic Algorithm is well-known as the optimizing algorithm taken after evolutionary strategy, and widely used for engineering problems that occur when designing a system by its self-organizing phenomenon. It is based on Neo-Darwinism, that is, the theory that individuals with advantageous characters to the given environment increase and individuals with disadvantageous ones decrease when mutation occurs in genetic characters in a population. In Genetic Algorithm, there is a premise that all the genetic characters in an organism have some meanings of adaptation. In the natural organisms, however, there are a number of neutral mutations, which is neither advantageous nor disadvantageous to their adaptation. This fact suggests that there is a new possibility of genetic mechanism in designing evolutionary systems. This is the framework motivated by the neutral theory of molecular evolution, which is different from the conventional design based on Neo-Darwinian Genetic Algorithm. In the neutral theory, some genetic characters in an individual are not positively possessed under selection pressure (not by orthogenesis), but allowed to be fixed by chance, remaining neutral to selection pressure (by random genetic drift). This leads to the supplement of redundancy to genetic information which represents an individual, and the appearance of the diversity of genetic information in a population. In this paper, in order to investigate the above point concretely, we adopt the Ladder-Network as a simple model which makes permutation of information. When we evaluate the fitness by using only the degree of correspondence between target alignment and output one, the factor of the number of steps in the network becomes the neutral character which does not directly affect the fitness.

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