Dissertation/Thesis Abstract

Multi-objective cultural algorithms
by Best, Christopher, M.S., Wayne State University, 2009, 53; 1465539
Abstract (Summary)

Multi-objective optimization is a widely applicable problem in Engineering and Computer Science. In the past, Cultural Algorithms have been used to solve complex optimization and design problems. In this thesis we extend the Cultural Algorithm Framework to support multi-objective problems. The resultant system, Multi-Objective Cultural Algorithms (MOCA), can be used independently or as a supplement to existing MO optimization methods. We compare the performance of our algorithm with NSGA-II using problems from the DTLZ test suite, a popular MOEA test suite. We found that Cultural Algorithms are a promising technique for solving multi-objective problems.

Indexing (document details)
Advisor: Reynolds, Robert G.
Commitee: Dong, Ming, Fisher, Nathan W.
School: Wayne State University
Department: Computer Science
School Location: United States -- Michigan
Source: MAI 47/06M, Masters Abstracts International
Subjects: Computer science
Keywords: Cultural algorithms, Multi-objective, Optimization
Publication Number: 1465539
ISBN: 978-1-109-21984-5
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