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检索条件"任意字段=11th European Conference on Evolutionary Computation in Combinatorial Optimization"

共 292 条记录，以下是41-50 订阅

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A Unifying View on Recombination Spaces and Abstract Convex evolutionary Search 1

引用

19th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Garcia, Marcos Diez Moraglio, Alberto Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

Previous work proposed to unify an algebraic theory of fitness landscapes and a geometric framework of evolutionary algorithms (EAs). One of the main goals behind this unification is to develop an analytical method that verifies if a problem's landscape belongs to certain abstract convex landscape classes, where certain recombination-based EAs (without mutation) have polynomial runtime performance. this paper advances such unification by showing that: (a) crossovers can be formally classified according to geometric or algebraic axiomatic properties;and (b) the population behaviour induced by certain crossovers in recombination-based EAs can be formalised in the geometric and algebraic theories. these results make a significant contribution to the basis of an integrated geometric-algebraic framework with which analyse recombination spaces and recombination based EAs.

关键词： Abstract convex landscape Abstract convex search Convex hull closure Geometric crossover Recombination P-structure

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A Cooperative optimization Approach for Distributing Service Points in Mobility Applications 1

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19th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Jatschka, thomas Rodemann, Tobias Raidl, Guenther R. TU Wien Inst Log & Computat Vienna Austria Honda Res Inst Europe Offenbach Germany

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

We investigate a variant of the facility location problem concerning the optimal distribution of service points with incomplete information within a certain geographical area. the application scenario is generic in principle, but we have the setup of charging stations for electric vehicles or rental stations for bicycles or cars in mind. When planning such systems, estimating under which conditions which customer demand can be fulfilled is fundamental in order to evaluate and optimize possible solutions. In this paper we present a cooperative optimization approach for distributing service points that incorporates potential customers not only in the data acquisition but also during the optimization process. A surrogate objective function is used to evaluate intermediate solutions during the optimization. the quality of this surrogate objective function is iteratively improved by learning from the feedback of potential users given to candidate solutions. For the actual optimization we consider a population based iterated greedy algorithm. Experiments on artificial benchmark scenarios with idealized simulated user behavior show the learning capabilities of the surrogate objective function and the effectiveness of the optimization.

关键词： Cooperative optimization Facility location problem Surrogate objective function Metaheuristics

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Clarifying the Difference in Local Optima Network Sampling Algorithms 1

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19th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： thomson, Sarah L. Ochoa, Gabriela Verel, Sebastien Univ Stirling Comp Sci & Math Stirling Scotland Univ Littoral Cote dOpale EA 4491 LISIC Calais France

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. these methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on a random-walk snowballing procedure, while the other is centred around multiple traced runs of an Iterated Local Search. Both of these are proposed for the Quadratic Assignment Problem (QAP), making this the focus of our study. It is important to note the sampling algorithm frameworks could easily be modified for other domains. In our study descriptive statistics for the obtained search space samples are contrasted and commented on. the LON features are also used in linear mixed models and random forest regression for predicting heuristic optimisation performance of two prominent heuristics for the QAP on the underlying combinatorial problems. the model results are then used to make deductions about the sampling algorithms' utility. We also propose a specific set of LON metrics for use in future predictive models alongside previously-proposed network metrics, demonstrating the payoff in doing so.

关键词： combinatorial fitness landscapes Local optima networks Quadratic Assignment Problem

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Quasi-Optimal Recombination Operator 1

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19th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Chicano, Francisco Ochoa, Gabriela Whitley, Darrell Tinos, Renato Univ Malaga Malaga Spain Univ Stirling Stirling Scotland Colorado State Univ Ft Collins CO 80523 USA Univ Sao Paulo Sao Paulo SP Brazil

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

the output of an optimal recombination operator for two parent solutions is a solution with the best possible value for the objective function among all the solutions fulfilling the gene transmission property: the value of any variable in the offspring must be inherited from one of the parents. this set of solutions coincides with the largest dynastic potential for the two parent solutions of any recombination operator with the gene transmission property. In general, exploring the full dynastic potential is computationally costly, but if the variables of the objective function have a low number of non-linear interactions among them, the exploration can be done in O(4(ss) (n + m) + n(2)) time, for problems with n variables, m subfunctions and ss a constant. In this paper, we propose a quasi-optimal recombination operator, called Dynastic Potential Crossover (DPX), that runs in O(4(ss) (n+ m)+ n(2)) time in any case and is able to explore the full dynastic potential for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with two recently defined efficient recombination operators: Partition Crossover (PX) and Articulation Points Partition Crossover (APX). the empirical comparison uses NKQ Landscapes and MAX-SAT instances.

关键词： Recombination operator Dynastic potential Gray box optimization

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evolutionary computation in combinatorial optimization: 16th european conference, EvoCOP 2016 Porto, Portugal, march 30 – april 1, 2016 proceedings 16th

Evolutionary computation in combinatorial optimization: 16th...

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16th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2016

作者： Chicano, Francisco Hu, Bin García-Sánchez, Pablo University of Málaga Málaga Spain Austrian Institute of Technology Vienna Austria University of Granada Granada Spain

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MOEA/DEP: An Algebraic Decomposition-Based evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem 1

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18th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Baioletti, Marco Milani, Alfredo Santucci, Valentino Univ Perugia Dept Math & Comp Sci Perugia Italy Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

Algebraic evolutionary algorithms are an emerging class of meta-heuristics for combinatorial optimization based on strong mathematical foundations. In 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 (MoPFSP). In particular, the makespan and total flowtime objectives have been investigated. Experiments have been held on a widely used benchmark suite, and the obtained results have been compared with respect to the state-of-the-art Pareto fronts for MoPFSP. the experimental results have been analyzed by means of two commonly used performance metrics for multiobjective optimization. the analysis clearly shows that MOEA/DEP reaches new state-of-the-art results for the considered benchmark.

关键词： Algebraic evolutionary algorithms Multiobjective optimization Permutation Flowshop Scheduling Problem

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Worst Improvement Based Iterated Local Search 1

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18th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Tari, Sara Basseur, Matthieu Goeffon, Adrien Lab Etud & Rech Informat Angers UFR Sci 2 Blvd Lavoisier F-49045 Angers 01 France

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

To solve combinatorial optimization problems, many metaheuristics use first or best improvement hill-climbing as intensification mechanism in order to find local optima. In particular, first improvement offers a good tradeoff between computation cost and quality of reached local optima. In this paper, we investigate a worst improvement-based moving strategy, never considered in the literature. Such a strategy is able to reach good local optima despite requiring a significant additional computation cost. Here, we investigate if such a pivoting rule can be efficient when considered within metaheuristics, and especially within iterated local search (ILS). In our experiments, we compare an ILS using a first improvement pivoting rule to an ILS using an approximated version of worst improvement pivoting rule. Both methods are launched with the same number of evaluations on bit-string based fitness landscapes. Results are analyzed using some landscapes' features in order to determine if the worst improvement principle should be considered as a moving strategy in some cases.

关键词： combinatorial optimization

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16th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2016

16th European Conference on Evolutionary Computation in Comb...

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16th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2016

ISBN: (纸本)9783319306971

the proceedings contain 17 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: A hybrid constructive mat-heuristic algorithm for the heterogeneous vehicle routing problem with simultaneous pick-up and delivery;a property preserving method for extending a single-objective problem instance to multiple objectives with specific correlations;an evolutionary approach to the full optimization of the traveling thief problem;application to the repetition-free longest common subsequence problem;deconstructing the big valley search space hypothesis;determining the difficulty of landscapes by pagerank centrality in local optima networks;efficient hill climber for multi-objective pseudo-Boolean optimization;evaluating hyperheuristics and local search operators for periodic routing problems;evolutionary algorithms for finding short addition chains;experimental evaluation of two approaches to optimal recombination for permutation problems;hyperplane elimination for quickly enumerating local optima;limits to learning in reinforcement learning hyper-heuristics;modifying colourings between time-steps to tackle changes in dynamic random graphs;particle swarm optimisation with sequence-like indirect representation for web service composition;particle swarm optimization for multi-objective web service location allocation;a multipopulation estimation of distribution algorithm based on problem similarity and solving the quadratic assignment problem with cooperative parallel extremal optimization.

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An evolutionary Algorithm with Practitioner's-Knowledge-Based Operators for the Inventory Routing Problem 1

引用

18th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Lipinski, Piotr Michalak, Krzysztof Univ Wroclaw Inst Comp Sci Computat Intelligence Res Grp Wroclaw Poland Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

this paper concerns the Inventory Routing Problem (IRP) which is an optimization problem addressing the optimization of transportation routes and the inventory 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. the population for the EA is initialized starting from a base solution which in this paper is generated by a heuristic, but can as well be a solution provided by a domain expert. Subsequently, feasibility-preserving moves are used to generate the initial population. In the paper dedicated recombination and mutation operators are proposed which aim at generating new solutions without loosing feasibility. In order to reduce the search space, solutions in the presented EA are encoded as lists of routes with the quantities to be delivered determined by a supplying policy. the presented work is a step towards utilizing domain knowledge in evolutionary computation. the EA presented in this paper employs mechanisms of solution initialization capable of generating a set of feasible initial solutions of the IRP in a reasonable time. Presented operators generate new feasible solutions effectively without requiring a repair mechanism.

关键词： Inventory Routing Problem Dedicated genetic operators Knowledge-based optimization Constrained optimization

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How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes 1

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18th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： McMenemy, Paul Veerapen, Nadarajen Ochoa, Gabriela Univ Stirling Comp Sci & Math Stirling Scotland

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

Local optima networks are a valuable tool used to analyse and visualise the global structure of combinatorial search spaces;in particular, the existence 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 employed by Chained-LK modifies the landscape's global structure, with the effect being markedly different for the two classes of instances. Our quantitative analysis shows that several funnel metrics have stronger correlations with Chained-LK success rate than the number of local optima, indicating that global structure clearly impacts search performance.

关键词： Local optima network Travelling salesman problem Chained-LK Perturbation strength combinatorial fitness landscape

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