Adam Nogaj

Abstract

This thesis examines the performance of genetic algorithm (GA) crossover techniques within two problems: n-queens with poison (NQWP) and processor scheduling (PS). Each problem was analyzed at sizes of 32, 64, and 128, referring to number of queens to be placed and number of single-time-unit processes to be scheduled, respectively. The specific crossover techniques studied were cycle crossover, order crossover, partially mapped crossover, merging crossover, and one-point, two-point, and uniform signature representation crossover, in addition to various greedy approaches. In conjunction with tests that vary crossover techniques, experimentation was performed to determine what percentage of problem constraints (poisoned squares for NQWP or precedence relationships between tasks for PS) makes the problems most difficult to solve, that is, the constraint densities at which optimal solutions require the highest number of GA fitness evaluations. While minor fluctuations in difficulty occur upon variations in fitness function and problem size, the NQWP problem is most difficult around a constraint density of 0.8 and the PS problem is most difficult around constraint densities of 0.2 to 0.3. Even within an individual problem, one crossover technique does not irreproachably outperform others. However, cycle crossover stands out in its performance in the PS problem while merging crossover and uniform signature crossovers most often perform well for NQWP.

Library of Congress Subject Headings

Genetic algorithms--Evaluation; Problem solving

Publication Date

2011

Document Type

Thesis

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Gaborski, Roger

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: QA402.5 .N64 2011

Campus

RIT – Main Campus

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