The effect of the dynamic reorganization of a finite state machine genome on the efficiency of a genetic algorithm.
Item
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Title
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The effect of the dynamic reorganization of a finite state machine genome on the efficiency of a genetic algorithm.
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Identifier
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AAI9917658
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identifier
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9917658
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Creator
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Hammerman, Natalie.
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Contributor
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Adviser: Robert Goldberg
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Date
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1999
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer Science | Artificial Intelligence
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Abstract
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Strategies for solving different types of problems can be represented as a finite state machine (FSM). In artificial life and artificial intelligence research, such problems use finite state machines as the genotype (operand) for genetic algorithms (GAs). Strategies which are FSM-specific and designed to improve the convergence of the genetic algorithm for FSM genomes are investigated. Because a single finite state machine has different representations (simply by changing state names), two reorganization operators (named SFS and MTF) were developed so that identical machines would appear the same and not have to compete for their share of the next generation. The operators were designed with the intent of enhancing schemata growth for an FSM genome by reorganizing a population of these machines during run time. Experiments were performed with these new operators in order to determine how they would affect the efficiency of genetic algorithms. Strategies were then developed to permit evaluation of the data. It was found that MTF outperforms the other methods when the average size of solutions is less than 45% of the genome size. Based on this evaluation, it is deemed that the MTF operator be incorporated into a GA application.
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Type
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dissertation
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Source
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PQT Legacy CUNY.xlsx
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degree
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Ph.D.
