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Evolution Strategies

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A Brief Introduction to Continuous Evolutionary Optimization

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

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Abstract

Many real-world problems are multimodal, which renders an optimization problem difficult to solve. Local search methods, i.e., methods that greedily improve solutions based on search in the neighborhood of a solution, often only find an arbitrary local optimum that is not guaranteed to be the global one.

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Notes

  1. 1.

    \({\mathcal {N}}(m,\sigma ^2)\) represents a randomly drawn Gaussian distributed number with expectation value \(m\) and standard deviation \(\sigma \).

  2. 2.

    i.e., high values w.r.t. the data structure.

  3. 3.

    The index \(j\) denotes the index of the \(j\)-th ranked individual of the \(\lambda \) offspring individuals w.r.t. an increasing sorting based on fitness \(f({\mathbf {x}}_j)\).

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  1. Department für Informatik, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

    Oliver Kramer

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  1. Oliver Kramer
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Kramer, O. (2014). Evolution Strategies. In: A Brief Introduction to Continuous Evolutionary Optimization. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-03422-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-03422-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03421-8

  • Online ISBN: 978-3-319-03422-5

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