Author Type

Graduate Student

Date of Award

1997

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Evett, Matthew P.

Abstract

Genetic Programming is an evolutionary technique for searching through the space of S-expressions for programs that represent optimal or acceptable solutions to a given problem. Genetic Programming often has difficulty in finding the appropriate numeric constants to use in leaf nodes of the S-expressions. This thesis describes the use of local search algorithms to search for numeric constants that will improve the S-expressions found by Genetic Programming. Three methods, Multi-Dimensional Hill Climbing, Vector Hill Climbing, and Numeric Mutation are combined with Genetic Programming to create hybrid systems. The performance of these hybrid systems is analyzed and future directions for improving Genetic Programming with the use of hybrid systems are discussed.

Extent

109 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 1997.|College of Engineering and Computer Science

Identifier

FADT15493

Format

pdf

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