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Titlebook: Evolutionary Computation in Combinatorial Optimization; 16th European Confer Francisco Chicano,Bin Hu,Pablo García-Sánchez Conference proce

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发表于 2025-3-21 18:07:12 | 显示全部楼层 |阅读模式
书目名称Evolutionary Computation in Combinatorial Optimization
副标题16th European Confer
编辑Francisco Chicano,Bin Hu,Pablo García-Sánchez
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation in Combinatorial Optimization; 16th European Confer Francisco Chicano,Bin Hu,Pablo García-Sánchez Conference proce
描述.Thisbook constitutes the refereed proceedings of the 16th European Conference onEvolutionary Computation in Combinatorial Optimization, EvoCOP 2016, held in Porto,Portugal, in March/April 2016, co-located with the Evo*2015 events EuroGP,EvoMUSART and EvoApplications..The17 revised full papers presented were carefully reviewed and selected from 44submissions. The papers cover methodology, applications and theoretical studies. Themethods included evolutionary and memetic algorithms, variable neighborhoodsearch, particle swarm optimization, hyperheuristics, mat-heuristic and otheradaptive approaches. Applications included both traditional domains, such asgraph coloring, vehicle routing, the longest common subsequence problem, thequadratic assignment problem; and new(er) domains such as the traveling thiefproblem, web service location, and finding short addition chains. Thetheoretical studies involved fitness landscape analysis, local search and recombinationoperator analysis, and the big valley search space hypothesis. Theconsideration of multiple objectives, dynamic and noisy environments was alsopresent in a number of articles..
出版日期Conference proceedings 2016
关键词Evolutionary algorithms; Hyper-heuristics; Metaheuristics; Multi-objective optimisation; Particle swarm
版次1
doihttps://doi.org/10.1007/978-3-319-30698-8
isbn_softcover978-3-319-30697-1
isbn_ebook978-3-319-30698-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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A Property Preserving Method for Extending a Single-Objective Problem Instance to Multiple Objectiv can specify the correlations between the generated the objectives. Different from existing instance generation methods the new method allows to keep certain properties of the original single-objective instance. In particular, we consider optimization problems where the objective is defined by a mat
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Construct, Merge, Solve and Adapt: Application to the Repetition-Free Longest Common Subsequence Pron subsequence problem. The applied algorithm, which is labelled ., ., . & ., generates sub-instances based on merging the solution components found in randomly constructed solutions. These sub-instances are subsequently solved by means of an exact solver. Moreover, the considered sub-instances are
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Deconstructing the Big Valley Search Space Hypothesis, show here that the idea of a single valley does not always hold. Instead the big valley seems to de-construct into several valleys, also called ‘funnels’ in theoretical chemistry. We use the local optima networks model and propose an effective procedure for extracting the network data. We conduct a
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Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks,ith the help of Local Optima Networks (LONs) with escape edges. As a predictor, we use the PageRank Centrality of the global optimum. Escape edges can be extracted with lower effort than the edges used in a previous study. Second, we show that the PageRank vector of a LON can be used to predict the
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Evolutionary Algorithms for Finding Short Addition Chains: Going the Distance,ce it is an .-hard problem. In this paper, we propose a genetic algorithm with a novel representation of solutions and new crossover and mutation operators to minimize the length of the addition chains corresponding to a given exponent. We also develop a repair strategy that significantly enhances t
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Experimental Evaluation of Two Approaches to Optimal Recombination for Permutation Problems,e Asymmetric Travelling Salesman Problem and the Makespan Minimization Problem on a Single Machine. All four optimal recombination problems under consideration are NP-hard but relatively fast exponential-time algorithms are known for solving them. The experimental evaluation carried out in this pape
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