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Machine Learning Approach to Evolutionary Computation

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Evolutionary Approach to Machine Learning and Deep Neural Networks

Abstract

This chapter gives several methods of evolutionary computation enhanced with machine learning techniques. The employed machine learning schemes are bagging, boosting, Gröbner bases, relevance vector machine, affinity propagation, SVM, and k-nearest neighbors. These are applied to the extension of GP (Genetic Programming), DE (Differential Evolution), and PSO (Particle Swarm Optimization).

It is intriguing that computer scientists use the term genotype and phenotype when talking about their programs.

(John Maynard Smith, The Origins of Life: From the Birth of Life to the Origin of Language)

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Notes

  1. 1.

    Introns are structures that do not affect fitness values. In GP, two types of introns are known: (1) semantic introns: code segments that are executed but have no effect on the overall result, e.g., (\(+\) 3 (− x x)), (2) syntactic introns: non-executed code segments, e.g., (and false (\(+\) 2 3)).

  2. 2.

    For all invertible matrices A, B, C, and D of correct sizes, \((A+BDC) ^{-1} =A ^{-1}-A ^{-1}B(D ^{-1} +CA ^{-1} B) ^{-1} CA ^{-1}\) holds true.

  3. 3.

    keijzer 6, 7, 8, and 9 are easy targets. keijzer1 is relatively easier compared with keijzer 2 and 3.

  4. 4.

    Other techniques include Artificial Bee Colony Programming (ABCP), GP with standard crossover (SC), GP with no same mate (NSM), GP with context aware crossover (CAC), GP with soft brood selection (SBS), GP with semantic similarity-based crossover (SSC). See [20] for details.

  5. 5.

    SVM-Light Support Vector Machine, http://svmlight.joachims.org/.

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  1. The University of Tokyo, Tokyo, Japan

    Hitoshi Iba

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  1. Hitoshi Iba
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Iba, H. (2018). Machine Learning Approach to Evolutionary Computation. In: Evolutionary Approach to Machine Learning and Deep Neural Networks. Springer, Singapore. https://doi.org/10.1007/978-981-13-0200-8_4

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  • DOI: https://doi.org/10.1007/978-981-13-0200-8_4

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