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Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems.

By Alexandra Melike Brintrup, Hideyuki Takagi, Ashutosh Tiwari and Jeremy J. Ramsden

Abstract

We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, and evaluate them with both simulated and real users. Combining human evaluation with an optimization system for engineering design enables us to embed domainspecific knowledge that is frequently hard to describe, i.e. subjective criteria, and design preferences. We introduce a new IGA technique to extend the previously introduced sequential single objective GA and multi-objective GA, viz. parallel IGA. Experimental evaluation of three algorithms with a multi-objective manufacturing plant layout design task shows that the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and that the multi-objective IGA gives the most diverse results and fastest convergence to a stable set of qualitatively optimum solutions, although the parallel IGA provides the best quantitative fitness convergence

Topics: innovative design, subjectivity, evolutionary computing
Publisher: Jointly by, Collegium Basilea (Institute of Advanced Study) and Association of Modern Scientific Investigation.
Year: 2006
OAI identifier: oai:dspace.lib.cranfield.ac.uk:1826/2528
Provided by: Cranfield CERES
Journal:

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