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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21-30 订阅

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the Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation optimization 25th

The Asteroid Routing Problem: A Benchmark for Expensive Blac...

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25th International conference on the Applications of evolutionary computation (EvoApplications) Held as Part of EvoStar conference

作者： Lopez-Ibanez, Manuel Chicano, Francisco Gil-Merino, Rodrigo Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9783031024627;9783031024610

Inspired by the recent 11th Global Trajectory Optimisation Competition, this paper presents the asteroid routing problem (ARP) as a realistic benchmark of algorithms for expensive bound-constrained black-box optimization in permutation space. Given a set of asteroids' orbits and a departure epoch, the goal of the ARP is to find the optimal sequence for visiting the asteroids, starting from Earth's orbit, in order to minimize both the cost, measured as the sum of the magnitude of velocity changes required to complete the trip, and the time, measured as the time elapsed from the departure epoch until visiting the last asteroid. We provide open-source code for generating instances of arbitrary sizes and evaluating solutions to the problem. As a preliminary analysis, we compare the results of two methods for expensive blackbox optimization in permutation spaces, namely, combinatorial Efficient Global optimization (CEGO), a Bayesian optimizer based on Gaussian processes, and Unbalanced Mallows Model (UMM), an estimation-of-distribution algorithm based on probabilistic Mallows models. We investigate the best permutation representation for each algorithm, either rank-based or order-based. Moreover, we analyze the effect of providing a good initial solution, generated by a greedy nearest neighbor heuristic, on the performance of the algorithms. the results suggest directions for improvements in the algorithms being compared.

关键词： Spacecraft trajectory optimization Unbalanced Mallows Model combinatorial Efficient Global optimization Estimation of distribution algorithms Bayesian optimization

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evolutionary Anytime Algorithms 25th

Evolutionary Anytime Algorithms

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25th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

作者： Aishwaryaprajna Rowe, Jonathan E. Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

ISBN: (纸本)9783031868481

evolutionary algorithms are anytime algorithms, as they can produce a viable solution at any time and, moreover, the quality of the solution improves over time. However, it may be hoped that more information can be gained when a solution is produced, even if the algorithm is stopped before the optimum has been found. For example, one might want to know which of the bit values in the current best solution are necessary for a good result, and which are still uncertain. We propose heuristics for efficiently gaining such information about bit values, in the context of the (1+1)EA. Along the way, we prove two useful general results. the first bounds the runtime for m parallel copies of a process. the second bounds the probability of having a non-optimal bit value at any given position when optimising a weakly monotonic function. Using these results, we prove bounds for the time it takes for our proposed heuristics to correctly identify bit values when the (1+1)EA runs on monotonic and hidden subset problems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Price-and-Branch Heuristic for Vector Bin Packing 25th

Price-and-Branch Heuristic for Vector Bin Packing

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25th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

作者： Wang, Ze Süß, Tim Popov, Nikolay Nagel, Lars Loughborough University LE11 3TU Loughborough United Kingdom Fulda University of Applied Science Fulda36037 Germany iC-Haus GmbH Bodenheim55294 Germany

ISBN: (纸本)9783031868481

Vector bin packing is an NP-hard problem in which a set of item vectors must be packed into a minimum number of bins such that, in each bin, the sum of the vectors does not exceed the bin’s vector capacity. Vector bin packing has many applications such as scheduling virtual machines in cloud computing. In this paper, we introduce the Pattern Heuristic, a price-and-branch heuristic which applies column generation. It creates a large pool of valid packings called patterns by running fast heuristics. then it optimally solves the integer linear program of finding a minimum set of patterns which covers the complete set of items. Despite its complexity, it finds optimal or near-optimal solutions for benchmark instances with 200 items in a matter of seconds. the pattern pool is created by a Local Search Heuristic which, while being very fast, achieves surprisingly good results. As a stand-alone heuristic, it is suitable for scenarios with real-time demands. the new heuristics are compared with algorithms from the literature in experiments using established benchmarks. they demonstrate a good trade-off between running time and packing quality. the Pattern Heuristic computes new optimal solutions for a 20-dimensional benchmark. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Integer linear programming

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11th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2011

11th European Conference on Evolutionary Computation in Comb...

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11th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2011

ISBN: (纸本)9783642203633

the proceedings contain 22 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: Frequency distribution based hyper-heuristic for the bin-packing problem;from adaptive to more dynamic control in evolutionary algorithms;geometric generalisation of surrogate model based optimisation to combinatorial spaces;GPU-based approaches for multiobjective local search algorithms. A case study: the flowshop scheduling problem;local search for mixed-integer nonlinear optimization: A methodology and an application;multi-start heuristics for the two-echelon vehicle routing problem;NILS: A neutrality-based iterated local search and its application to flowshop scheduling;Off-line and on-line tuning: A study on operator selection for a memetic algorithm applied to the QAP;on complexity of the optimal recombination for the travelling salesman problem;a hybrid dual-population genetic algorithm for the single machine maximum lateness problem;Pareto local optima of multiobjective NK-landscapes with correlated objectives;Quick-ACO: Accelerating ant decisions and pheromone updates in ACO;two iterative metaheuristic approaches to dynamic memory allocation for embedded systems;a kolmogorov-type stability measure for evolutionary algorithms;a matheuristic approach for the total completion time two-machines permutation flow shop problem;connectedness and local search for bicriteria knapsack problems;cutting graphs using competing ant colonies and an edge clustering heuristic;effective variable fixing and scoring strategies for binary quadratic programming;evolutionary multiobjective route planning in dynamic multi-hop ridesharing;experiments in parallel constraint-based local search.

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Multiobjective optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations 23

Multiobjective Optimization Analysis for Finding Infrastruct...

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11th International conference on Computer and Communications Management, ICCCM 2023

作者： Osaba, Eneko Diaz-De-Arcaya, Josu Alonso, Juncal Lobo, Jesus L. Benguria, Gorka Etxaniz, Iñaki Tecnalia Derio Bizkaia 48160 Spain

ISBN: (纸本)9798400707735

Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. the design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. this paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. the system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. the main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. this is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 european project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs. © 2023 ACM.

关键词： Multiobjective optimization

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18th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2018

18th European Conference on Evolutionary Computation in Comb...

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18th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2018

ISBN: (纸本)9783319774480

the proceedings contain 12 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: A multistart alternating tabu search for commercial districting;an ant colony approach for the winner determination problem;on the fractal nature of local optima networks;How perturbation strength shapes the global structure of TSP fitness landscapes;worst improvement based iterated local search;automatic grammar-based design of heuristic algorithms for unconstrained binary quadratic programming;automatic algorithm configuration for the permutation flow shop scheduling problem minimizing total completion time;data clustering using grouping hyper-heuristics;reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling;MOEA/DEP: An algebraic decomposition-based evolutionary algorithm for the multiobjective permutation flowshop scheduling problem.

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Probing LLMs on optimization Problems: Can they Recall and Interpret Problem Features? 28th

Probing LLMs on Optimization Problems: Can They Recall and...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Da Ros, Francesca Di Gaspero, Luca Roitero, Kevin University of Udine Udine Italy

ISBN: (纸本)9783031900648

In this study, we explore the ability of Large Language Models (LLMs) to understand and recall features associated with combinatorial optimization problems in both Natural Language Processing (NLP) and structured contexts. By probing LLMs using a diverse set of optimization problem instances, we aim to evaluate the models’ ability to accurately extract and reason about key attributes, such as parameters and features. Our methodology involves both structured and extended NLP-based prompts for the models and instructing these models to identify specific features from the provided problem instances. the results reveal that while LLMs exhibit some capacity to identify and extract information, they fail to recall 100% of even the simplest features consistently present within the text. this limitation underscores the current challenges LLMs face in precise reasoning and feature extraction tasks, suggesting the need for further refinement in their interpretability and understanding capabilities when applied to structured problem-solving domains. (Relevant data and code are available at the following link: https://***/fw6ta/?view_only=d8e63cdda6bd409b83aa3d9a4b025b06). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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Injecting combinatorial optimization into MCTS: Application to the Board Game Boop 28th

Injecting Combinatorial Optimization into MCTS: Application ...

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28th european conference on Applications of evolutionary computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Richoux, Florian AIST Tokyo Japan

ISBN: (纸本)9783031900648

Games, including abstract board games, constitute a convenient ground to create, design, and improve new AI methods. In this field, Monte Carlo Tree Search is a popular algorithm family, aiming to build game trees and explore them efficiently. combinatorial optimization, on the other hand, aims to model and solve problems with an objective to optimize and constraints to satisfy, and is less common in Game AI. We believe however that both methods can be combined efficiently, by injecting combinatorial optimization into Monte Carlo Tree Search to help the tree search, leading to a novel combination of these two techniques. Tested on the board game boop., our method beats 96% of the time the Monte Carlo Tree Search algorithm baseline. We conducted an ablation study to isolate and analyze which injections and combinations of injections lead to such performances. Finally, we opposed our AI method against human players on the Board Game Arena platform, and reached a 373 ELO rating after 51 boop. games, with a 69% win rate and finishing ranked 56th worldwide on the platform over 5,316 boop. players. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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Learning General Policies from Small Examples Without Supervision 35

Learning General Policies from Small Examples Without Superv...

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35th AAAI conference on Artificial Intelligence / 33rd conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Frances, Guillem Bonet, Blai Geffner, Hector Univ Pompeu Fabra Barcelona Spain ICREA Barcelona Spain

ISBN: (纸本)9781577358664

Generalized planning is concerned with the computation of general policies that solve multiple instances of a planning domain all at once. It has been recently shown that these policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning problem (QNP) is learned from sample plans, then the general policies are obtained from the learned QNP using a planner. In this work, we introduce an alternative approach for computing more expressive general policies which does not require sample plans or a QNP planner. the new formulation is very simple and can be cast in terms that are more standard in machine learning: a large but finite pool of features is defined from the predicates in the planning examples using a general grammar, and a small subset of features is sought for separating "good" from "bad" state transitions, and goals from non-goals. the problems of finding such a "separating surface" while labeling the transitions as "good" or "bad" are jointly addressed as a single combinatorial optimization problem expressed as a Weighted Max-SAT problem. the advantage of looking for the simplest policy in the given feature space that solves the given examples, possibly non-optimally, is that many domains have no general, compact policies that are optimal. the approach yields general policies for a number of benchmark domains.

关键词： combinatorial optimization

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A Novel Unfeasible Space Exploring Matheuristic Proposal to Solve the Sum Coloring Problem 14th

A Novel Unfeasible Space Exploring Matheuristic Proposal to ...

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14th International conference on computational Collective Intelligence (ICCCI)

作者： Harrabi, Olfa Siala, Jouhaina Chaouachi Tunis Univ Higher Inst Management Tunis 41 Liberty St Bouchoucha 2000 Bardo Tunisia Carthage Univ Inst Adv Business Studies Carthage IHEC Carthage Presidency 2017 Tunis Carthage Tunisia

ISBN: (纸本)9783031162107;9783031162091

this research paper deals with a novel matheuristic approach to solve the Sum Coloring Problem. this problem is an extension of the classic graph coloring problem that simply focuses on coloring a given graph with the minimum number of colors. Indeed, the sum coloring problem is a hard-to-solve combinatorial optimization problem that imposes a minimal sum of colors where colors are integers. the work deals with a two-phase infeasible space matheuristic approach for solving the examined problem. the first phase relies on a relaxation of the sum coloring problem using a weight-based formulation to enable the exploration of non feasible solutions and diversify the conducted search. In the second phase, we refer to a hybrid evolutionary approach that efficiently explores the search space and oscillates between the feasible and unfeasible solutions. the computational performance of our approach, based on a set of benchmark instances, turned out to be effective and demonstrate that operating with non feasible solutions leads to promising results on both computation time and solution quality.

关键词： Sum coloring problem Chromatic sum Weight-based formulation Matheuristic evolutionary algorithm Simulated annealing

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