By Mike Preuss
This booklet deals the 1st accomplished taxonomy for multimodal optimization algorithms, paintings with its root in subject matters equivalent to niching, parallel evolutionary algorithms, and worldwide optimization.
The writer explains niching in evolutionary algorithms and its advantages; he examines their suitability to be used as diagnostic instruments for experimental research, particularly for detecting challenge (type) houses; and he measures and compares the performances of niching and canonical EAs utilizing diversified benchmark attempt challenge units. His paintings consolidates the hot successes during this area, providing and explaining use situations, algorithms, and function measures, with a spotlight all through at the targets of the optimization strategies and a deep knowing of the algorithms used.
The ebook could be beneficial for researchers and practitioners within the quarter of computational intelligence, really these engaged with heuristic seek, multimodal optimization, evolutionary computing, and experimental analysis.
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Example text
State that getting more than one good solution may improve our understanding of the objective function itself. When aiming at extracting knowledge, obtaining a broad spectrum 2 Detecting here means testing (by computing function values) if it really exists. This excludes estimating optimizers based on a learned model we are confident in. 12 1 Introduction: Towards Multimodal Optimization of optimizers may be helpful. However, in this work we give priority to searching for a small set of best possible solutions, if possible containing the global optimum.
De Jong [57] provides a more general perspective. Note that, differently from evolution strategies, canonical GA variants with binary representation need fixed search space bounds and minimal step accuracies, whereas an ES can be employed as an unconstrained optimization algorithm. As of 2014, it seems that the covariance matrix adaptation evolution strategy (CMAES), originally conceived by Hansen and Ostermeier in 2001 [103], is the predominant evolution strategy and probably the most used evolutionary optimization algorithm in the continuous domain.
Motwani and Raghavan [155] provide a detailed overview. Interestingly, theory in this area is also growing, not least due to the large collaborative research project SFB 5311 that took place at TU Dortmund from 1996 to 2008. This may be one of the reasons for the breakthrough of algorithms containing randomness in the 1990s. 2 Multimodal Optimization What exactly is the task when we speak of multimodal optimization? When tackling a multimodal problem, we may address three related but different issues.