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Current and Future Research Trends in Evolutionary Multiobjective Optimization

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Information Processing with Evolutionary Algorithms

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

In this chapter we present a brief analysis of the current research performed on evolutionary multiobjective optimization. After analyzing first- and second-generation multiobjective evolutionary algorithms, we address two important issues: the role of elitism in evolutionary multiobjective optimization and the way in which concepts from multiobjective optimization can be applied to constraint-handling techniques. We conclude with a discussion of some of the most promising research trends in the years to come.

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Authors and Affiliations

  1. CINVESTAV-IPN, Av. Instituto Politécnico, Nacional No. 2508, México, D.F. 07300, Mexico

    C. A. Coello Coello, G. Toscano Pulido & E. Mezura Montes

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  1. C. A. Coello Coello
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  2. G. Toscano Pulido
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  3. E. Mezura Montes
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Editors and Affiliations

  1. Dept. CCIA, Universidad Pais Vasco, Spain

    Manuel Graña BSc, MSc, PhD  & Alicia d’Anjou BSc, MSc, PhD  & 

  2. Grupo de Sistemas Autónomos, Universidade da Coruña, Spain

    Richard J. Duro BSc, MSc, PhD

  3. Duke University, Durham, North Carolina, USA

    Paul P. Wang PhD

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Coello Coello, C.A., Pulido, G.T., Montes, E.M. (2005). Current and Future Research Trends in Evolutionary Multiobjective Optimization. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_15

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  • DOI: https://doi.org/10.1007/1-84628-117-2_15

  • Publisher Name: Springer, London

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