Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition

  • Carlos A. Coello Coello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

This paper provides a brief introduction to the so-called multi-objective evolutionary algorithms, which are bio-inspired metaheuristics designed to deal with problems having two or more (normally conflicting) objectives. First, we provide some basic concepts related to multi-objective optimization and a brief review of approaches available in the specialized literature. Then, we provide a short review of applications of multi-objective evolutionary algorithms in pattern recognition. In the final part of the paper, we provide some possible paths for future research in this area, which are promising, from the author’s perspective.

Keywords

Particle Swarm Optimization Pareto Front Multiobjective Optimization Multiobjective Optimization Problem Nondominated Solution 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos A. Coello Coello
    • 1
  1. 1.Departamento de ComputaciónCINVESTAV (Evolutionary Computation Group)México, D.F.Mexico

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