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Titlebook: Evolutionary Computation in Combinatorial Optimization; 18th European Confer Arnaud Liefooghe,Manuel López-Ibáñez Conference proceedings 20

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发表于 2025-3-21 19:15:58 | 显示全部楼层 |阅读模式
书目名称Evolutionary Computation in Combinatorial Optimization
副标题18th European Confer
编辑Arnaud Liefooghe,Manuel López-Ibáñez
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation in Combinatorial Optimization; 18th European Confer Arnaud Liefooghe,Manuel López-Ibáñez Conference proceedings 20
描述.This book constitutes the refereed proceedings of the 18th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events EuroGP, EvoMUSART and EvoApplications... The 12 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration by means of automatic algorithm configuration and hyper-heuristics. Applicationscover conventional academic domains such as NK landscapes, binary quadratic programming, traveling salesman, vehicle routing, or scheduling problems, and also include real-world domains in clustering, commercial districting and winner determination..
出版日期Conference proceedings 2018
关键词artificial intelligence; automatic algorithm configuration; combinatorial optimization; evolutionary al
版次1
doihttps://doi.org/10.1007/978-3-319-77449-7
isbn_softcover978-3-319-77448-0
isbn_ebook978-3-319-77449-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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On the Fractal Nature of Local Optima Networks,ere have been recent observations made about inherent .. An object is said to be self-similar if it shows the same patterns when measured at different scales; another word used to convey self-similarity is .. The . of an object captures how the detail observed changes with the scale at which it is m
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How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes,nce and distribution of multiple funnels in the landscape. We extract and analyse the networks induced by Chained-LK, a powerful iterated local search for the TSP, on a large set of randomly generated (Uniform and Clustered) instances. Results indicate that increasing the perturbation strength emplo
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Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Totaln flow shop scheduling problem minimizing total completion time with a context-free grammar that defines how algorithmic components can be combined to form a full heuristic search method. We implement components from various works from the literature, including several local search procedures. The s
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Data Clustering Using Grouping Hyper-heuristics,ultiple criteria varying dependent on the domain. A recent work proposed a general-purpose selection hyper-heuristic search framework with reusable components, designed for rapid development of grouping hyper-heuristics to solve grouping problems. The framework was tested only on the graph colouring
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Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop he literature that JSS usually contains many (four or more) potentially conflicting objectives. One of the promising and successful approaches to solve the JSS problem is Genetic Programming Hyper-Heuristic (GP-HH). This approach automatically evolves dispatching rules for solving JSS problems. This
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MOEA/DEP: An Algebraic Decomposition-Based Evolutionary Algorithm for the Multiobjective Permutatio this paper we introduce a decomposition-based algebraic evolutionary algorithm, namely MOEA/DEP, in order to deal with multiobjective permutation-based optimization problems. As a case of study, MOEA/DEP has been experimentally validated on a multiobjective permutation flowshop scheduling problem (
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,An Evolutionary Algorithm with Practitioner’s-Knowledge-Based Operators for the Inventory Routing Pinventory levels at the same time. The IRP is notable for its difficulty - even finding feasible initial solutions poses a significant problem..In this paper an evolutionary algorithm is proposed that uses approaches to solution construction and modification utilized by practitioners in the field. T
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