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evolutionary computation in combinatorial optimization 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Peter Merz Jin-Kao Hao

ISBN: (数字)9783642203640

ISBN: (纸本)9783642203633

this book constitutes the refereed proceedings of the 11th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. the 22 revised full papers presented were carefully reviewed and selected from 42 submissions. the papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

关键词： Algorithm Analysis and Problem Complexity computation by Abstract Devices Pattern Recognition Discrete Mathematics in Computer Science Probability and Statistics in Computer Science

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A study of some properties of Ant-Q 4th

A study of some properties of Ant-Q

引用

International conference on evolutionary computation - 4th International conference on Parallel Problem Solving from Nature, PPSN 1996

作者： Dorigo, Marco Gambardella, Luca Maria IDSIA Corso Elvezia 36 LuganoCH-6900 Switzerland

ISBN: (纸本)354061723X

Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimization problems. the main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms. © 1996, Springer-Verlag. All rights reserved.

关键词： combinatorial optimization

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Exact computation of the fitness-distance correlation for pseudoboolean functions with one global optimum

Exact computation of the fitness-distance correlation for ps...

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12th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2012

作者： Chicano, Francisco Alba, Enrique University of Málaga Spain

ISBN: (纸本)9783642291234

Landscape theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of a special kind of landscapes called elementary landscapes. the decomposition of the objective function of a problem into its elementary components can be exploited to compute summary statistics. We present closed-form expressions for the fitness-distance correlation (FDC) based on the elementary landscape decomposition of the problems defined over binary strings in which the objective function has one global optimum. We present some theoretical results that raise some doubts on using FDC as a measure of problem difficulty. © 2012 Springer-Verlag.

关键词： combinatorial optimization

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On the Fractal Nature of Local Optima Networks 1

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

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

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. 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 fractal. the fractal dimension of an object captures how the detail observed changes with the scale at which it is measured, with a high fractal dimension being associated with complexity. We conduct a detailed study on the fractal nature of the local optima networks of a benchmark combinatorial optimisation problem (NK Landscapes). the results draw connections between fractal characteristics and performance by three prominent metaheuristics: Iterated Local Search, Simulated Annealing, and Tabu Search.

关键词： combinatorial fitness landscapes Local optima networks Fractal analysis NK Landscapes

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evolutionary Strategies for the Design of Binary Linear Codes 1

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23rd european conference on evolutionary computation in combinatorial Optimisation (EvoCOP) Held as Part of EvoStar conference

作者： Carlet, Claude Mariot, Luca Manzoni, Luca Picek, Stjepan Univ Paris Dept Math 2 Rue Libert prime F-93526 St Denis France Univ Bergen Bergen Norway Univ Twente Semant Cybersecur Serv Grp Drienerlolaan 5 NL-7522 NB Enschede Netherlands Univ Trieste Dept Math & Geosci Via Valerio 12-1 Trieste Italy Radboud Univ Nijmegen Digital Secur Grp POB 9010 Nijmegen Netherlands

ISBN: (数字)9783031300356

ISBN: (纸本)9783031300349;9783031300356

the design of binary error-correcting codes is a challenging optimization problem with several applications in telecommunications and storage, which has been addressed with metaheuristic techniques such as evolutionary algorithms. Still, all these efforts are focused on optimizing the minimum distance of unrestricted binary codes, i.e., with no constraints on their linearity, which is a desirable property for efficient implementations. In this paper, we present an evolutionary Strategy (ES) algorithm that explores only the subset of linear codes of a fixed length and dimension. We represent the candidate solutions as binary matrices and devise variation operators that preserve their ranks. Our experiments show that up to length n = 14, our ES always converges to an optimal solution with a full success rate, and the evolved codes are all inequivalent to the Best-Known Linear Code (BKLC) given by MAGMA. On the other hand, for larger lengths, both the success rate of the ES as well as the diversity of the codes start to drop, with the extreme case of (16, 8, 5) codes which all turn out to be equivalent to MAGMA's BKLC.

关键词： Error-correcting codes Boolean functions Algebraic normal form evolutionary strategies Variation operators

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Emergence of New Local Search Algorithms with Neuro-Evolution 24th

Emergence of New Local Search Algorithms with Neuro-Evolutio...

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Goudet, Olivier Sakhri, Mohamed Salim Amri Goeffon, Adrien Saubion, Frederic Univ Angers LERIA SFR MATHSTIC F-49000 Angers France

ISBN: (纸本)9783031577116;9783031577123

this paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. the main objective is to better manage information within these algorithms, while retaining simplicity and generality in their core components. Our goal is to equip a neural network with the same information as the basic local search and, after a training phase, use the neural network as the fundamental move component within a straightforward local search process. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. this approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems.

关键词： Neuro-evolution Local search Black-box optimization NK landscapes

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Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling 1

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

作者： Masood, Atiya Chen, Gang Mei, Yi Zhang, Mengjie Victoria Univ Wellington Wellington New Zealand

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

Job Shop Scheduling (JSS) is considered to be one of the most significant combinatorial optimization problems in practice. It is widely evidenced in the 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 paper aims to evolve a set of effective dispatching rules for many-objective JSS with genetic programming and NSGA-III. NSGA-III originally defines uniformly distributed reference points in the objective space. thus, there will be few reference points with no Pareto optimal solutions associated with them;especially, in the cases with discrete and non-uniform Pareto front, resulting in many useless reference points during evolution. In other words, these useless reference points adversely affect the performance of NSGA-III and genetic programming. To address the above issue, in this paper a new reference point adaptation mechanism is proposed based on the distribution of the candidate solutions. We evaluated the performance of the proposed mechanism on many-objective benchmark JSS instances. Our results clearly show that the proposed strategy is promising in adapting reference points and outperforms the existing state-of-the-art algorithms for many-objective JSS.

关键词： Job Shop Scheduling Many-objective optimization Genetic programming Reference points

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Exploring multi-objective PSO and GRASP-PR for rule induction

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8th european conference on evolutionary computation in combinatorial optimization

作者： Ishida, Celso Y. de Carvalho, Andre B. Pozo, Aurora T. R. Goldbarg, Elizabeth F. G. Goldbarg, Marco C. Univ Fed Parana BR-80060000 Curitiba Parana Brazil Univ Fed Rio Grande do Norte BR-59072970 Natal RN Brazil

ISBN: (纸本)9783540786030

this paper presents a method of classification rule discovery based on two multiple objective metaheuristics: a Greedy Randomized Adaptive Search Procedure with path-relinking (GRASP-PR), and Multiple Objective Particle Swarm (MOPS). the rules are selected at the creation rule process following Pareto dominance concepts and forming unordered classifiers. We compare our results with other well known rule induction algorithms using the area under the ROC curve. the multi-objective metaheuristic algorithms results are comparable to the best known techniques. We are working on different parallel schemes to handle large databases, these aspects will be subject of future works.

关键词： Particle swarm optimization (PSO)

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evolutionary computation in combinatorial optimization 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Jin-Kao Hao Martin Middendorf

ISBN: (数字)9783642291241

ISBN: (纸本)9783642291234

this book constitutes the refereed proceedings of the 12th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. . the 22 revised full papers presented were carefully reviewed and selected from 48 submissions. the papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

关键词： Algorithm Analysis and Problem Complexity computation by Abstract Devices Pattern Recognition Discrete Mathematics in Computer Science Probability and Statistics in Computer Science

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An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem 1

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21st european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Le, Hoang thanh Middendorf, Martin Shi, Yuhui Univ Leipzig Inst Comp Sci Swarm Intelligence & Complex Syst Grp Leipzig Germany Southern Univ Sci & Technol Comp Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9783030729042

ISBN: (纸本)9783030729035;9783030729042

the Dynamic Orienteering Problem (DOP) is studied where nodes change their value over time. An improvement heuristic that is based on Variable Neighborhood Search is proposed for the DOP. the new heuristic is experimentally compared with two heuristics that are based on state-of-the-art algorithms for the static Orienteering Problem. For the experiments several benchmark instances are used as well as instances that are generated from existing road networks. the results show that the new heuristic outperforms the other heuristics with respect to several evaluation criteria and different measures for run time. An additional experiment shows that the new heuristic can be easily adapted to become a standalone algorithm that does not need given initial solutions. the standalone version obtains better results than two state-of-the-art algorithms.

关键词： Dynamic optimization Neighborhood search Anytime algorithms Routing problems

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