Multimodal Optimization by Means of Evolutionary Algorithms by Mike Preuss

By Mike Preuss

This booklet deals the 1st entire taxonomy for multimodal optimization algorithms, paintings with its root in subject matters corresponding to niching, parallel evolutionary algorithms, and worldwide optimization.

The writer explains niching in evolutionary algorithms and its merits; he examines their suitability to be used as diagnostic instruments for experimental research, in particular for detecting challenge (type) houses; and he measures and compares the performances of niching and canonical EAs utilizing diverse benchmark try out challenge units. His paintings consolidates the new successes during this area, proposing and explaining use circumstances, algorithms, and function measures, with a spotlight all through at the objectives of the optimization methods and a deep realizing of the algorithms used.

The ebook could be necessary for researchers and practitioners within the zone of computational intelligence, fairly these engaged with heuristic seek, multimodal optimization, evolutionary computing, and experimental analysis.

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3, highlighting the need for a structured approach with well-defined aims, parameter settings, designs, and measures. Finally, Sect. 4 deals with the positive aspect of parameters: the possibility of adapting algorithms to concrete needs via new, more suitable parameter settings. 1 Preamble: Justification for a Methodology Why care about methodological questions concerning experimentation, especially in Evolutionary Computation? At first glance, devoting a full chapter of this work to the experimental methodology seems to exaggerate its importance.

Many older and some new scientific writings in the field of evolutionary computation have preferred an on-the-fly description of the employed experimental techniques or completely omitted such information. We argue that especially stochastic algorithms with many parameters require carefully designed and evaluated experiments. Although past studies in EC have often suggested new algorithms and experimentally substantiated their claim of suitability for a certain purpose, most of these have rapidly disappeared.

These cannot be found in [16] because they were published much later. Chapter 3 turns to the existing niching definitions in evolutionary computation and their relation to the biological archetypes. We then set up a very simple model algorithm in order to explore the limit behaviors. This takes up ideas of [168] and extends them. Interestingly, some of the more simple cases can be computed instead of simulated due to their similarity with the coupon collector’s problem (CCP), for which a number of theoretical and numerical works exist.

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