ABSTRACT

MULTI-OBJECTIVE CULTURAL ALGORITHMS

by

CHRISTOPHER BEST

August 2009

Advisor: Dr. Robert Reynolds

Major: Computer Science

Degree: Master of Science

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.