Selmar Smit

Selmar Smit

The Hague, South Holland, Netherlands
2K followers 500+ connections

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Vrije Universiteit Amsterdam

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Specialized in predicting human (criminal) behavior using model-based machine…

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Volunteer Experience

  • Board

    STORM - Student Organisation

    - 6 years 1 month

    Science and Technology

    • vice‐president
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Publications

  • Parameter Tuning and Scientific Testing in Evolutionary Algorithms

    Vrije Universiteit, Amsterdam

    In this thesis, the issue of evaluating and comparing evolutionary algorithms is adressed, and more specific, the role of parameter tuning in it. A more scientific testing approach is promoted in which the focus is on why and when an algorithm performs better, rather than if it is better. Such an approach is not only more informative for the users of algorithms, but also changes the work of researchers from development into science.

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  • Parameter Tuning for Configuring and Analyzing Evolutionary Algorithms

    Swarm and Evolutionary Computation, 1(1):19-31

    In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish di?fferent taxonomies…

    In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish di?fferent taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis.

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