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Evolutionary Computation and Meta-heuristics

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Deep Neural Evolution

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Abstract

This chapter presents several methods of evolutionary computation and meta-heuristics. Evolutionary computation is a computation technique that mimics the evolutionary mechanism of life to select, deform, and convolute data structures. Because of its high versatility, its applications are found in various fields. Meta-heuristics described in this chapter are considered as representatives of swarm intelligence, such as particle swarm optimization (PSO), artificial bee colony optimization (ABC), ant colony optimization (ACO), firefly algorithms, cuckoo search, etc. A benefit of these methods is global searching as well as local searching. Existence of local minima or saddle points could lead to a locally optimum solution when using gradient methods such as the steepest descent search. By contrast, the methods described in this chapter can escape from such local solutions by means of various kinds of operations. Methods of evolutionary computation and meta-heuristics are used in combination with deep learning to establish a framework of deep neural evolution, which will be described in later chapters.

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Notes

  1. 1.

    Parasitized species are almost always fixed for each female cuckoo.

  2. 2.

    Furthermore, a cuckoo chick having just been hatched expels all the eggs of its host. For this reason, a cuckoo chick has a pit in its back to place its host’s egg, clambers up inside the nest and throw the egg out of the nest. This behavior was discovered by Edward Jenner, famous for smallpox vaccination.

  3. 3.

    It corresponds to allele in genotype under GA.

  4. 4.

    David Hilbert (1862–1943): German mathematician. At the second International Congress of Mathematicians (ICM) in Paris in 1900, he made a speech on “problems in mathematics,” where he stressed the importance of 23 unsolved problems and presented a prospect for future creative research through these problems. Some of them continue to be themes for research on mathematics and computer science.

  5. 5.

    Further discussions have been presented, and there is an insistence on the part of some individuals that it is a different method. Refer to https://en.wikipedia.org/wiki/Harmony_search and [12] for details.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan

    Hitoshi Iba

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  1. Hitoshi Iba
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Correspondence to Hitoshi Iba .

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  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Hitoshi Iba

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Nasimul Noman

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Iba, H. (2020). Evolutionary Computation and Meta-heuristics. In: Iba, H., Noman, N. (eds) Deep Neural Evolution. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-15-3685-4_1

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  • DOI: https://doi.org/10.1007/978-981-15-3685-4_1

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