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Unpacking and Understanding Evolutionary Algorithms

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7311)

Abstract

Theoretical analysis of evolutionary algorithms (EAs) has made significant progresses in the last few years. There is an increased understanding of the computational time complexity of EAs on certain combinatorial optimisation problems. Complementary to the traditional time complexity analysis that focuses exclusively on the problem, e.g., the notion of NP-hardness, computational time complexity analysis of EAs emphasizes the relationship between algorithmic features and problem characteristics. The notion of EA-hardness tries to capture the essence of when and why a problem instance class is hard for what kind of EAs. Such an emphasis is motivated by the practical needs of insight and guidance for choosing different EAs for different problems. This chapter first introduces some basic concepts in analysing EAs. Then the impact of different components of an EA will be studied in depth, including selection, mutation, crossover, parameter setting, and interactions among them. Such theoretical analyses have revealed some interesting results, which might be counter-intuitive at the first sight. Finally, some future research directions of evolutionary computation will be discussed.

Keywords

IEEE Transaction Evolutionary Algorithm Problem Size Evolutionary Computation Memetic Algorithm 
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 2012

Authors and Affiliations

  • Xin Yao
    • 1
  1. 1.CERCIA, School of Computer ScienceUniversity of BirminghamEdgbastonUK

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