Evolutionary Computation最新文献

All-Quadratic Mixed-Integer Problems: A Study on Evolution Strategies and Mathematical Programming. 全二次混合整数问题：进化策略与数学规划的研究。

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-10 DOI: 10.1162/evco.a.29

Guy Zepko, Ofer M Shir

{"title":"All-Quadratic Mixed-Integer Problems: A Study on Evolution Strategies and Mathematical Programming.","authors":"Guy Zepko, Ofer M Shir","doi":"10.1162/evco.a.29","DOIUrl":"https://doi.org/10.1162/evco.a.29","url":null,"abstract":"Mixed-integer (MI) quadratic models subject to quadratic constraints, known as All- Quadratic MI Programs, constitute a challenging class of NP-complete optimization problems. The particular scenario of unbounded integers defines a subclass that holds the distinction of being even undecidable. This complexity suggests a possible soft-spot for Mathematical Programming (MP) techniques, which otherwise constitute a good choice to treat MI problems. We consider the task of minimizing MI convex quadratic objective and constraint functions with unbounded decision variables. Given the theoretical weakness of white-box MP solvers to handle such models, we turn to black-box meta-heuristics of the Evolution Strategies (ESs) family, and question their capacity to solve this challenge. Through an empirical assessment of all-quadratic test-cases, across varying Hessian forms and condition numbers, we compare the performance of the CPLEX solver to modern MI ESs, which handle constraints by penalty. Our systematic investigation begins where the CPLEX solver encounters difficulties (timeouts as the search-space dimensionality increases, D < 30), and we report in detail on the D = 64 case. Overall, the empirical observations confirm that black-box and white-box solvers can be competitive over this MI problem class, exhibiting 67% similar performance in terms of the attained objective function values in a fixed-budget perspective. Despite consistent termination in timeouts, CPLEX demonstrated superior or comparable performance to the MIESs in 98% of the cases. This trend is flipped when unboundedness is amplified by a significant translation of the optima, leading to a totally inferior performance of CPLEX across 81% of the cases. We also conclude that conditioning and separability are not intuitive factors in determining the hardness degree of this MI problem class.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-27"},"PeriodicalIF":3.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034668","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

P-NP Instance Decomposition Based on the Fourier Transform for Solving the Linear Ordering Problem. 基于傅里叶变换的P-NP实例分解求解线性排序问题。

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-02 DOI: 10.1162/evco_a_00368

Xabier Benavides, Leticia Hernando, Josu Ceberio, Jose A Lozano

{"title":"P-NP Instance Decomposition Based on the Fourier Transform for Solving the Linear Ordering Problem.","authors":"Xabier Benavides, Leticia Hernando, Josu Ceberio, Jose A Lozano","doi":"10.1162/evco_a_00368","DOIUrl":"10.1162/evco_a_00368","url":null,"abstract":"The Fourier transform over finite groups has proved to be a useful tool for analyzing combinatorial optimization problems. However, few heuristic and metaheuristic algorithms have been proposed in the literature that utilize the information provided by this technique to guide the search process. In this work, we attempt to address this research gap by considering the case study of the Linear Ordering Problem (LOP). Based on the Fourier transform, we propose an instance decomposition strategy that divides any LOP instance into the sum of two LOP instances associated with a P and an NP-Hard optimization problem. By linearly aggregating the instances obtained from the decomposition, it is possible to create artificial instances with modified proportions of the P and NP-Hard components. Conducted experiments show that increasing the weight of the P component leads to a less rugged fitness landscape suitable for local search-based optimization. We take advantage of this phenomenon by presenting a new metaheuristic algorithm called P-Descent Search (PDS). The proposed method, first, optimizes a surrogate instance with a high proportion of the P component, and then, gradually increases the weight of the NP-Hard component until the original instance is reached. The multi-start version of PDS shows a promising and predictable performance that appears to be correlated to specific characteristics of the problem, which could open the door to an automatic tuning of its hyperparameters.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"395-423"},"PeriodicalIF":3.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469897","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

Genetic Programming for Automatically Evolving Multiple Features to Classification. 遗传编程自动演化分类的多重特征

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-02 DOI: 10.1162/evco_a_00359

Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang

引用次数: 0

Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multiobjective Algorithms. 利用进化多目标算法优化单调机会受限子模函数

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-02 DOI: 10.1162/evco_a_00360

Aneta Neumann, Frank Neumann

{"title":"Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multiobjective Algorithms.","authors":"Aneta Neumann, Frank Neumann","doi":"10.1162/evco_a_00360","DOIUrl":"10.1162/evco_a_00360","url":null,"abstract":"Many real-world optimization problems can be stated in terms of submodular functions. Furthermore, these real-world problems often involve uncertainties which may lead to the violation of given constraints. A lot of evolutionary multiobjective algorithms following the Pareto optimization approach have recently been analyzed and applied to submodular problems with different types of constraints. We present a first runtime analysis of evolutionary multiobjective algorithms based on Pareto optimization for chance-constrained submodular functions. Here the constraint involves stochastic components and the constraint can only be violated with a small probability of α. We investigate the classical GSEMO algorithm for two different bi-objective formulations using tail bounds to determine the feasibility of solutions. We show that the algorithm GSEMO obtains the same worst case performance guarantees for monotone submodular functions as recently analyzed greedy algorithms for the case of uniform IID weights and uniformly distributed weights with the same dispersion when using the appropriate bi-objective formulation. As part of our investigations, we also point out situations where the use of tail bounds in the first bi-objective formulation can prevent GSEMO from obtaining good solutions in the case of uniformly distributed weights with the same dispersion if the objective function is submodular but non-monotone due to a single element impacting monotonicity. Furthermore, we investigate the behavior of the evolutionary multiobjective algorithms GSEMO, NSGA-II, and SPEA2 on different submodular chance-constrained network problems. Our experimental results show that the use of evolutionary multiobjective algorithms leads to significant performance improvements compared to state-of-the-art greedy algorithms for submodular optimization.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"363-393"},"PeriodicalIF":3.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331627","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

Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions. 以低维标度化函数替代物为指导的大规模多目标进化算法

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-02 DOI: 10.1162/evco_a_00354

Haoran Gu, Handing Wang, Cheng He, Bo Yuan, Yaochu Jin

{"title":"Large-Scale Multiobjective Evolutionary Algorithm Guided by Low-Dimensional Surrogates of Scalarization Functions.","authors":"Haoran Gu, Handing Wang, Cheng He, Bo Yuan, Yaochu Jin","doi":"10.1162/evco_a_00354","DOIUrl":"10.1162/evco_a_00354","url":null,"abstract":"Recently, computationally intensive multiobjective optimization problems have been efficiently solved by surrogate-assisted multiobjective evolutionary algorithms. However, most of those algorithms can handle no more than 200 decision variables. As the number of decision variables increases further, unreliable surrogate models will result in a dramatic deterioration of their performance, which makes large-scale expensive multiobjective optimization challenging. To address this challenge, we develop a large-scale multiobjective evolutionary algorithm guided by low-dimensional surrogate models of scalarization functions. The proposed algorithm (termed LDS-AF) reduces the dimension of the original decision space based on principal component analysis, and then directly approximates the scalarization functions in a decomposition-based multiobjective evolutionary algorithm. With the help of a two-stage modeling strategy and convergence control strategy, LDS-AF can keep a good balance between convergence and diversity, and achieve a promising performance without being trapped in a local optimum prematurely. The experimental results on a set of test instances have demonstrated its superiority over eight state-of-the-art algorithms on multiobjective optimization problems with up to 1,000 decision variables using only 500 real function evaluations.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"309-334"},"PeriodicalIF":3.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421720","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

On the Use of the Doubly Stochastic Matrix Models for the Quadratic Assignment Problem. 双随机矩阵模型在二次分配问题中的应用。

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-09-02 DOI: 10.1162/evco_a_00369

Valentino Santucci, Josu Ceberio

{"title":"On the Use of the Doubly Stochastic Matrix Models for the Quadratic Assignment Problem.","authors":"Valentino Santucci, Josu Ceberio","doi":"10.1162/evco_a_00369","DOIUrl":"10.1162/evco_a_00369","url":null,"abstract":"Permutation problems have captured the attention of the combinatorial optimization community for decades due to the challenge they pose. Although their solutions are naturally encoded as permutations, in each problem, the information to be used to optimize them can vary substantially. In this paper, we consider the Quadratic Assignment Problem (QAP) as a case study, and propose using Doubly Stochastic Matrices (DSMs) under the framework of Estimation of Distribution Algorithms. To that end, we design efficient learning and sampling schemes that enable an effective iterative update of the probability model. Conducted experiments on commonly adopted benchmarks for the QAP prove doubly stochastic matrices to be preferred to the other four models for permutations, both in terms of effectiveness and computational efficiency. Moreover, additional analyses performed on the structure of the QAP and the Linear Ordering Problem (LOP) show that DSMs are good to deal with assignment problems, but they have interesting capabilities to deal also with ordering problems such as the LOP. The paper concludes with a description of the potential uses of DSMs for other optimization paradigms, such as genetic algorithms or model-based gradient search.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"425-457"},"PeriodicalIF":3.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469891","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

Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization. 黑箱优化中多样性与适应度的权衡。

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2025-07-30 DOI: 10.1162/evco.a.28

Maria Laura Santoni, Elena Raponi, Aneta Neumann, Frank Neumann, Mike Preuss, Carola Doerr

{"title":"Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.","authors":"Maria Laura Santoni, Elena Raponi, Aneta Neumann, Frank Neumann, Mike Preuss, Carola Doerr","doi":"10.1162/evco.a.28","DOIUrl":"https://doi.org/10.1162/evco.a.28","url":null,"abstract":"In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is therefore important to consider more solutions that decision makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether they have been specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to understand the capability of off-the-shelf algorithms to quantify the trade-off between the minimum pairwise distance within batches of solutions and their average quality. We also analyze how this trade-off depends on the properties of the underlying optimization problem. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-21"},"PeriodicalIF":3.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754976","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

Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-Objective and Multiobjective Continuous Optimization Problems. 基于自监督预训练变压器的单目标和多目标连续优化问题深度探索性景观分析。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-04 DOI: 10.1162/evco_a_00372

Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann

{"title":"Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-Objective and Multiobjective Continuous Optimization Problems.","authors":"Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann","doi":"10.1162/evco_a_00372","DOIUrl":"https://doi.org/10.1162/evco_a_00372","url":null,"abstract":"In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, i.a., from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is-to the best of our knowledge-very limited. Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1) a strong correlation between multiple features, as well as (2) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data. Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multiobjective optimization problems. Our proposed framework can either be used out-of-the-box for analyzing single- and multi-objective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-29"},"PeriodicalIF":4.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295248","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

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm 使用模糊逻辑的超参数控制：自适应模糊粒子群优化算法的演化策略。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1162/evco_a_00353

Nicolas Roy;Charlotte Beauthier;Alexandre Mayer

引用次数: 0

Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm 分类紧凑遗传算法运行时间的尾边界

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1162/evco_a_00361

Ryoki Hamano;Kento Uchida;Shinichi Shirakawa;Daiki Morinaga;Youhei Akimoto

引用次数: 0