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one max function analysis, genetic algorithms, gene invariance

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Subject-Keyword: one max function analysis genetic algorithms gene invariance

Type of item: Computing Science Technical Report

Computing science technical report ID: TR92-05

Language: English

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Description: Technical report TR92-05. Genetic algorithms are adaptive search algorithms which generate and test a population of individuals, where each individual corresponds to a solution. They then adapt to the information obtained from testing, seeking superior solutions by selecting and combining solutions of above average value. As the number of superior individuals in the population increases, the number of inferior individuals decreases. This thesis introduces Genetic Invariance, a similar family of generate and test problem solvers which uses a different selection and replacement strategy. In the best case, it achieves superior solutions without eliminating inferior characteristics. Although characteristics may initially be associated with inferior solutions, they may prove to be superior when combined with other particular characteristics. Mathematical analysis of lower bounds of Genetic Invariance on a simple function is given, and several properties of Genetic Invariance are explained using this analysis. A comparison and contrast is done to show how the two selection strategies achieve optimization in different ways. An analysis of the assumption and strategies of each system explains likely beneficial and detrimental effects of each system, while empirical analysis is given which demonstrates these effects. Together, they show each system-s features and drawbacks. See also: Joseph Culberson; \-GIGA Program Description and Operation\- June 1992 Technical Report TR92-06 Joseph Culberson; \-Genetic Invariance: A New Paradigm for Genetic Algorithm Design\- June 1992 Technical Report TR92-02

Date created: 1992

DOI: doi:10.7939-R3SB3X233

License information: Creative Commons Attribution 3.0 Unported

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Author: Lewchuk, Michael

Source: https://era.library.ualberta.ca/


Teaser



Genetic Invariance: A New Type of Genetic Algorithm Michael J.
Lewchuk April 1992 Abstract Genetic algorithms are adaptive search algorithms which generate and test a population of individuals, where each individual corresponds to a solution.
They then adapt to the information obtained from testing, seeking superior solutions by selecting and combining solutions of above average value.
As the number of superior individuals in the population increases, the number of inferior individuals decreases.
This thesis introduces Genetic Invariance, a similar family of generate and test problem solvers which uses a dierent selection and replacement strategy.
In the best case, it achieves superior solutions without eliminating inferior characteristics.
Although characteristics may initially be associated with inferior solutions, they may prove to be superior when combined with other particular characteristics.
Mathematical analysis of lower bounds of Genetic Invariance on a simple function is given, and several properties of Genetic Invariance are explained using this analysis.
A comparison and contrast is done to show how the two selection strategies achieve optimization in dierent ways.
An analysis of the assumption and strategies of each system explains likely benecial and detrimental eects of each system, while empirical analysis is given which demonstrates these eects.
Together, they show each systems features and drawbacks. i Contents 1 Genetic Algorithms : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 : 1 : 4 : 4 : 7 : 8 : 10 2.1 Introduction : : : : : : : : : : : : : : : : : : : 2.2 The Structure of Genetic Invariance : : : : : 2.3 Mathematical Analysis : : : : : : : : : : : : : 2.3.1 Introduction : : : : : : : : : : : : : : 2.3.2 A Special Case : : : : : : : : : : : : : 2.3.3 Improving the Algorithm : : : : : : : 2.3.4 Extending the Special Case : : : : : : 2.3.5 Put...





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