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Titlebook: Adaptive and Multilevel Metaheuristics; Carlos Cotta,Marc Sevaux,Kenneth Sörensen Book 2008 Springer-Verlag Berlin Heidelberg 2008 Layer.P

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期刊全称Adaptive and Multilevel Metaheuristics
影响因子2023Carlos Cotta,Marc Sevaux,Kenneth Sörensen
视频video
发行地址Presents recent results in Adaptive and Multilevel Metaheuristics
学科分类Studies in Computational Intelligence
图书封面Titlebook: Adaptive and Multilevel Metaheuristics;  Carlos Cotta,Marc Sevaux,Kenneth Sörensen Book 2008 Springer-Verlag Berlin Heidelberg 2008 Layer.P
影响因子.One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics...These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc...Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multile
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https://doi.org/10.1007/978-3-662-40307-5ing (SA) method. Our objective is to allow a decision maker to efficiently use advanced operation research techniques without a deep knowledge of this domain. Classical SA controls the probability of acceptance using an a priori temperature scheduling (Temperature Driven SA, or TD-SA). In this paper
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Teresa M. Buck,Jeffrey L. Brodsky any assumptions about the function to be optimized, apart from the availability of evaluations at selected test points. The main design criterion of the Reactive Affine Shaker (RASH) scheme consists of the adaptation of a search region by an affine transformation. The modification takes into accoun
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Bernoulli Shift and Related Concepts,oaches when assigning orders to vehicles. The resulting clustering problem is studied within a general framework for multi-objective vehicle routing problems where different vehicle agents place bids for orders which are offered on a marketplace. This marketplace gathers information about the curren
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