12. Multiobjective Evolutionary Algorithms

In this chapter we describe the application of evolutionary techniques
to a particular class of problems, namely
multiobjective optimisation. We begin by introducing this class of
problems and the particularly important notion of Pareto
optimality. We then look at some of the current state-of-the-art
multiobjective EAs (MOEAs) for this class of problems and examine the
ways in which they make use of concepts of different evolutionary
spaces and techniques for promoting and preserving diversity within
the population.

Contents
12.1 Multiobjective Optimisation Problems …………………195
12.2 Dominance and Pareto Optimality…………………….196
12.3 EA Approaches to Multiobjective Optimisation . . . . . . . . . . 198
12.3.1 Nonelitist Approaches…………………………198
12.3.2 Elitist Approaches……………………………199
12.3.3 Diversity Maintenance in MOEAs ……………….199
12.3.4 Decomposition-Based Approaches ……………….200
12.4 Example Application: Distributed Coevolution of Job Shop Schedules………………………………………..200

Suggested Reading

Exercises

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The on-line accompaniment to the book Introduction to Evolutionary Computing