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Memetic Computation pp 17–26Cite as

Canonical Memetic Algorithms

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Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 21))

Abstract

The remarkable flexibility of evolutionary computation (EC) in handling a wide range of problems, encompassing search, optimization, and machine learning, opens up a path to attaining artificial general intelligence. However, it is clear that excessive reliance on purely stochastic evolutionary processes, with no expert guidance or external knowledge incorporation, will often lead to performance characteristics that are simply too slow for practical applications demanding near real-time operations. What is more, the randomness associated with classical evolutionary algorithms (EAs) implies that they may not be the ideal tool of choice for various applications relying on high precision and crisp performance guarantees. These observations provided the impetus for conceptualizing the memetic computation (MC) paradigm, wherein the basic mechanisms of evolution are augmented with domain-knowledge expressed as computationally encoded memes. In this chapter, we introduce what is perhaps the most recognizable algorithmic realization of MC, namely, the canonical memetic algorithm (CMA).

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Abhishek Gupta

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yew-Soon Ong

Authors
  1. Abhishek Gupta
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  2. Yew-Soon Ong
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Correspondence to Abhishek Gupta .

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Gupta, A., Ong, YS. (2019). Canonical Memetic Algorithms. In: Memetic Computation. Adaptation, Learning, and Optimization, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-02729-2_2

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