Eugenics-Based Genetic Algorithm

Ju YE; Masahiro TANAKA; Tetsuzo TANINO

Eugenics-Based Genetic Algorithm

Ju YE, Masahiro TANAKA, Tetsuzo TANINO

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The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F₂, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.

Publication: IEICE TRANSACTIONS on Information Vol.E79-D No.5 pp.600-607

Publication Date: 1996/05/25

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Type of Manuscript: PAPER

Category: Artificial Intelligence and Cognitive Science

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Ju YE, Masahiro TANAKA, Tetsuzo TANINO, "Eugenics-Based Genetic Algorithm" in IEICE TRANSACTIONS on Information, vol. E79-D, no. 5, pp. 600-607, May 1996, doi: .
Abstract: The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F₂, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.
URL: https://global.ieice.org/en_transactions/information/10.1587/e79-d_5_600/_p

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@ARTICLE{e79-d_5_600,
author={Ju YE, Masahiro TANAKA, Tetsuzo TANINO, },
journal={IEICE TRANSACTIONS on Information},
title={Eugenics-Based Genetic Algorithm},
year={1996},
volume={E79-D},
number={5},
pages={600-607},
abstract={The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F₂, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Eugenics-Based Genetic Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 600
EP - 607
AU - Ju YE
AU - Masahiro TANAKA
AU - Tetsuzo TANINO
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E79-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1996
AB - The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F₂, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.
ER -