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A Simple Approach to Evolutionary Multiobjective Optimization

  • Christine L. Mumford-Valenzuela
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Summary

This chapter describes a Pareto-based approach to evolutionary multiobjective optimization, that avoids most of the time-consuming global calculations typical of other multi-objective evolutionary techniques. The new approach uses a simple uniform selection strategy within a steady-state evolutionary algorithm (EA) and employs a straightforward elitist mechanism for replacing population members with their offspring. Global calculations for fitness and Pareto dominance are not needed. Other state-of-the-art Pareto-based EAs depend heavily on various fitness functions and niche evaluations, mostly based on Pareto dominance, and the calculations involved tend to be rather time consuming (at least O(N2) for a population size, N). The new approach has performed well on some benchmark combinatorial problems and continuous functions, outperforming the latest state-of-the-art EAs in several cases. In this chapter the new approach will be explained in detail.

Keywords

Genetic Algorithm Pareto Front Multiobjective Optimization Knapsack Problem Solution Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag London Limited 2005

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  • Christine L. Mumford-Valenzuela

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