Evolutionary Computation最新文献_第4页

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. 受约束的重要性：处理微分进化论中的不可行解及其他问题

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_a_00333

Anna V Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A Mitran, Daniela Zaharie

{"title":"The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond.","authors":"Anna V Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A Mitran, Daniela Zaharie","doi":"10.1162/evco_a_00333","DOIUrl":"10.1162/evco_a_00333","url":null,"abstract":"We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours in terms of performance, disruptiveness, and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants, on a special test function and the BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem dimensionality. Differential Evolution is not at all special in this regard-there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the heuristic optimisation community to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we refer to as the strategy of dealing with infeasible solutions. This component needs to be consistently: (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on an algorithm's performance in a wider sense (i.e., convergence time, robustness, etc.), and (c) included in the (automatic) design of algorithms. All of these should be done even for problems with bound constraints.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"3-48"},"PeriodicalIF":6.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9474478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Decomposed Error for Reproducing Implicit Understanding of Algorithms. 利用分解错误重现对算法的隐性理解。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_a_00321

Caitlin A Owen, Grant Dick, Peter A Whigham

{"title":"Using Decomposed Error for Reproducing Implicit Understanding of Algorithms.","authors":"Caitlin A Owen, Grant Dick, Peter A Whigham","doi":"10.1162/evco_a_00321","DOIUrl":"10.1162/evco_a_00321","url":null,"abstract":"Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly, multiple runs of an algorithm, without a fixed random seed, should ideally return statistically equivalent results. Secondly, it should be confirmed whether the expected behaviour of an algorithm matches its actual behaviour, in terms of how an algorithm targets a reduction in prediction error. Confirming the behaviour of an algorithm is not possible when using a total error aggregate score. Using an error decomposition framework as a methodology for improving the reproducibility of results in evolutionary computation addresses both of these factors. By estimating decomposed error using multiple runs of an algorithm and multiple training sets, the framework provides a greater degree of certainty about the prediction error. Also, decomposing error into bias, variance due to the algorithm (internal variance), and variance due to the training data (external variance) more fully characterises evolutionary algorithms. This allows the behaviour of an algorithm to be confirmed. Applying the framework to a number of evolutionary algorithms shows that their expected behaviour can be different to their actual behaviour. Identifying a behaviour mismatch is important in terms of understanding how to further refine an algorithm as well as how to effectively apply an algorithm to a problem.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"49-68"},"PeriodicalIF":6.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9084698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data. BUSTLE：从数据中进化学习 STL 规格的多功能工具。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-02-19 DOI: 10.1162/evco_a_00347

Federico Pigozzi, Laura Nenzi, Eric Medvet

{"title":"BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data.","authors":"Federico Pigozzi, Laura Nenzi, Eric Medvet","doi":"10.1162/evco_a_00347","DOIUrl":"https://doi.org/10.1162/evco_a_00347","url":null,"abstract":"Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive and allows state properties as human-readable formulae. Crafting STL formulae that fit a particular system is, however, a difficult task. For this reason, a few approaches have been proposed recently for the automatic learning of STL formulae starting from observations of the system. In this paper, we propose BUSTLE (Bi-level Universal STL Evolver), an approach based on evolutionary computation for learning STL formulae from data. BUSTLE advances the state-of-the-art because it (i) applies to a broader class of problems, in terms of what is known about the state of the system during its observation, and (ii) generates both the structure and the values of the parameters of the formulae employing a bi-level search mechanism (global for the structure, local for the parameters). We consider two cases where (a) observations of the system in both anomalous and regular state are available, or (b) only observations of regular state are available. We experimentally evaluate BUSTLE on problem instances corresponding to the two cases and compare it against previous approaches. We show that the evolved STL formulae are effective and human-readable: the versatility of BUSTLE does not come at the cost of lower effectiveness.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-24"},"PeriodicalIF":6.8,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139913984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic Optimization. 一种数据流集成辅助的多因子进化算法用于离线数据驱动的动态优化。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-12-01 DOI: 10.1162/evco_a_00332

Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai

{"title":"A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic Optimization.","authors":"Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai","doi":"10.1162/evco_a_00332","DOIUrl":"10.1162/evco_a_00332","url":null,"abstract":"Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://github.com/Peacefulyang/DSE_MFS.git.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"433-458"},"PeriodicalIF":6.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9424656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives. 多模态目标下多目标进化算法的理论分析。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-12-01 DOI: 10.1162/evco_a_00328

Weijie Zheng, Benjamin Doerr

{"title":"Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives.","authors":"Weijie Zheng, Benjamin Doerr","doi":"10.1162/evco_a_00328","DOIUrl":"10.1162/evco_a_00328","url":null,"abstract":"Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards a deeper understanding of how evolutionary algorithms solve multimodal multiobjective problems, we propose the OneJumpZeroJump problem, a bi-objective problem composed of two objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multiobjective optimizer (SEMO) with probability one does not compute the full Pareto front, regardless of the runtime. In contrast, for all problem sizes n and all jump sizes k∈[4..n2-1], the global SEMO (GSEMO) covers the Pareto front in an expected number of Θ((n-2k)nk) iterations. For k=o(n), we also show the tighter bound 32enk+1±o(nk+1), which might be the first runtime bound for an MOEA that is tight apart from lower-order terms. We also combine the GSEMO with two approaches that showed advantages in single-objective multimodal problems. When using the GSEMO with a heavy-tailed mutation operator, the expected runtime improves by a factor of at least kΩ(k). When adapting the recent stagnation-detection strategy of Rajabi and Witt (2022) to the GSEMO, the expected runtime also improves by a factor of at least kΩ(k) and surpasses the heavy-tailed GSEMO by a small polynomial factor in k. Via an experimental analysis, we show that these asymptotic differences are visible already for small problem sizes: A factor-5 speed-up from heavy-tailed mutation and a factor-10 speed-up from stagnation detection can be observed already for jump size 4 and problem sizes between 10 and 50. Overall, our results show that the ideas recently developed to aid single-objective evolutionary algorithms to cope with local optima can be effectively employed also in multiobjective optimization.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"337-373"},"PeriodicalIF":6.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9250961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 36

Upgrades of Genetic Programming for Data-Driven Modeling of Time Series. 时间序列数据驱动建模中遗传规划的改进。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-12-01 DOI: 10.1162/evco_a_00330

A Murari, E Peluso, L Spolladore, R Rossi, M Gelfusa

{"title":"Upgrades of Genetic Programming for Data-Driven Modeling of Time Series.","authors":"A Murari, E Peluso, L Spolladore, R Rossi, M Gelfusa","doi":"10.1162/evco_a_00330","DOIUrl":"10.1162/evco_a_00330","url":null,"abstract":"In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modeling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behavior or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"401-432"},"PeriodicalIF":6.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9395244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems. 树状高斯过程回归求解离线数据驱动的连续多目标优化问题。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-12-01 DOI: 10.1162/evco_a_00329

Atanu Mazumdar, Manuel López-Ibáñez, Tinkle Chugh, Jussi Hakanen, Kaisa Miettinen

{"title":"Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems.","authors":"Atanu Mazumdar, Manuel López-Ibáñez, Tinkle Chugh, Jussi Hakanen, Kaisa Miettinen","doi":"10.1162/evco_a_00329","DOIUrl":"10.1162/evco_a_00329","url":null,"abstract":"For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for example, a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"375-399"},"PeriodicalIF":6.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9395245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symmetry Breaking for Voting Mechanisms* 投票机制的对称性打破。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-09-01 DOI: 10.1162/evco_a_00327

Preethi Sankineni;Andrew M. Sutton

{"title":"Symmetry Breaking for Voting Mechanisms*","authors":"Preethi Sankineni;Andrew M. Sutton","doi":"10.1162/evco_a_00327","DOIUrl":"10.1162/evco_a_00327","url":null,"abstract":"Recently, Rowe and Aishwaryaprajna (2019) introduced a simple majority vote technique that efficiently solves Jump with large gaps, OneMax with large noise, and any monotone function with a polynomial-size image. In this paper, we identify a pathological condition for this algorithm: the presence of spin-flip symmetry in the problem instance. Spin-flip symmetry is the invariance of a pseudo-Boolean function to complementation. Many important combinatorial optimization problems admit objective functions that exhibit this pathology, such as graph problems, Ising models, and variants of propositional satisfiability. We prove that no population size exists that allows the majority vote technique to solve spin-flip symmetric functions of unitation with reasonable probability. To remedy this, we introduce a symmetry-breaking technique that allows the majority vote algorithm to overcome this issue for many landscapes. This technique requires only a minor modification to the original majority vote algorithm to force it to sample strings in {0,1}n from a dimension n-1 hyperplane. We prove a sufficient condition for a spin-flip symmetric function to possess in order for the symmetry-breaking voting algorithm to succeed, and prove its efficiency on generalized TwoMax, a spin-flip symmetric variant of Jump, and families of constructed 3-NAE-SAT and 2-XOR-SAT formulas. We also prove that the algorithm fails on the one-dimensional Ising model, and suggest different techniques for overcoming this. Finally, we present empirical results that explore the tightness of the runtime bounds and the performance of the technique on randomized satisfiability variants.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"31 3","pages":"309-335"},"PeriodicalIF":6.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10121476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-Optimization 基于二维预优化的三维建筑质量多样性高效优化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-09-01 DOI: 10.1162/evco_a_00326

Alexander Hagg;Martin L. Kliemank;Alexander Asteroth;Dominik Wilde;Mario C. Bedrunka;Holger Foysi;Dirk Reith

{"title":"Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-Optimization","authors":"Alexander Hagg;Martin L. Kliemank;Alexander Asteroth;Dominik Wilde;Mario C. Bedrunka;Holger Foysi;Dirk Reith","doi":"10.1162/evco_a_00326","DOIUrl":"10.1162/evco_a_00326","url":null,"abstract":"Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing hundreds of thousands of evaluations. Even with the assistance of surrogate models, quality diversity needs hundreds or even thousands of evaluations, which can make its use infeasible. In this study, we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1,024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"31 3","pages":"287-307"},"PeriodicalIF":6.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10514699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approaching the Traveling Tournament Problem with Randomized Beam Search 用随机束搜索求解巡回比赛问题

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2023-09-01 DOI: 10.1162/evco_a_00319

Nikolaus Frohner;Bernhard Neumann;Giulio Pace;Günther R. Raidl

{"title":"Approaching the Traveling Tournament Problem with Randomized Beam Search","authors":"Nikolaus Frohner;Bernhard Neumann;Giulio Pace;Günther R. Raidl","doi":"10.1162/evco_a_00319","DOIUrl":"10.1162/evco_a_00319","url":null,"abstract":"The traveling tournament problem is a well-known sports league scheduling problem famous for its practical hardness. Given an even number of teams with symmetric distances between their venues, a double round-robin tournament has to be scheduled minimizing the total travel distances over all teams. We consider the most common constrained variant without repeaters and a streak limit of three, for which we study a beam search approach based on a state-space formulation guided by heuristics derived from different lower bound variants. We solve the arising capacitated vehicle routing subproblems either exactly for small- to medium-sized instances up to 18 teams or heuristically also for larger instances up to 24 teams. In a randomized variant of the search, we employ random team ordering and add small amounts of Gaussian noise to the nodes' guidance for diversification when multiple runs are performed. This allows for a simple yet effective parallelization of the beam search. A final comparison is done on the NL, CIRC, NFL, and GALAXY benchmark instances with 12 to 24 teams, for which we report a mean gap difference to the best known feasible solutions of 1.2% and five new best feasible solutions.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"31 3","pages":"233-257"},"PeriodicalIF":6.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10143062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1