Evolutionary Algorithms and Hyper-Heuristics

  • Rodrigo C. BarrosEmail author
  • André C. P. L. F. de Carvalho
  • Alex A. Freitas
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


This chapter presents the basic concepts of evolutionary algorithms (EAs) and hyper-heuristics (HHs), which are computational techniques directly explored in this book. EAs are well-known population-based metaheuristics. They have been employed in artificial intelligence over several years with the goal of providing the near-optimal solution for a problem that comprises a very large search space. A general overview of EAs is presented in Sect. 3.1. HHs, in turn, are a recently new field in the optimisation research area, in which a metaheuristic—often an EA, and this is why these related concepts are reviewed together in this chapter—is used for searching in the space of heuristics (algorithms), and not in the space of solutions, like conventional metaheuristics. The near-optimal heuristic (algorithm) provided by a HHs approach can be further employed in several distinct problems, instead of relying on a new search process for each new problem to be solved. An overview of HHs is given in Sect. 3.2.


Evolutionary algorithms Evolutionary computation Metaheuristics Hyper-heuristics 


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Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Rodrigo C. Barros
    • 1
    Email author
  • André C. P. L. F. de Carvalho
    • 2
  • Alex A. Freitas
    • 3
  1. 1.Faculdade de InformáticaPontifícia Universidade Católica do Rio Grande do SulPorto AlegreBrazil
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  3. 3.School of ComputingUniversity of KentCanterburyUK

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