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Multi-Objective Evolutionary Algorithms: Past, Present, and Future

In: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Author

Listed:
  • Carlos A. Coello Coello

    (CINVESTAV-IPN)

  • Silvia González Brambila

    (UAM Azcapotzalco)

  • Josué Figueroa Gamboa

    (UAM Azcapotzalco)

  • Ma. Guadalupe Castillo Tapia

    (UAM Azcapotzalco)

Abstract

Evolutionary algorithms have become a popular choice for solving highly complex multi-objective optimization problems in recent years. Multi-objective evolutionary algorithms were originally proposed in the mid-1980s, but it was until the mid-1990s when they started to attract interest from researchers. Today, we have a wide variety of algorithms, and research in this area has become highly specialized. This chapter attempts to provide a general overview of multi-objective evolutionary algorithms, starting from their early origins, then moving in chronological order towards some of the most recent algorithmic developments. In the last part of the chapter, some future research paths on this topic are briefly discussed.

Suggested Citation

  • Carlos A. Coello Coello & Silvia González Brambila & Josué Figueroa Gamboa & Ma. Guadalupe Castillo Tapia, 2021. "Multi-Objective Evolutionary Algorithms: Past, Present, and Future," Springer Optimization and Its Applications, in: Panos M. Pardalos & Varvara Rasskazova & Michael N. Vrahatis (ed.), Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, pages 137-162, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-66515-9_5
    DOI: 10.1007/978-3-030-66515-9_5
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