Evolutionary Computation最新文献

Preference-Driven Surrogate-Assisted Evolutionary Multiobjective Feature Selection. 偏好驱动的代理辅助进化多目标特征选择。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-09-30 DOI: 10.1162/EVCO.a.408

Zhenshou Song, Handing Wang, Bing Xue, Mengjie Zhang, Yaochu Jin

{"title":"Preference-Driven Surrogate-Assisted Evolutionary Multiobjective Feature Selection.","authors":"Zhenshou Song, Handing Wang, Bing Xue, Mengjie Zhang, Yaochu Jin","doi":"10.1162/EVCO.a.408","DOIUrl":"https://doi.org/10.1162/EVCO.a.408","url":null,"abstract":"Enhancing the classification performance is more challenging than reducing the number of features in multiobjective feature selection. Such imbalance might lead the population to converge to a region on the Pareto front with a small number of features. Meanwhile, increasing the classification performance is time consuming, especially when the training data contain a large number of instances and features. Surrogate models can help reduce the optimization time. However, they need to be well-designed to accurately approximate such complex search space. Over-reliance on surrogate models, especially those with large approximation errors, can mislead the population toward local optima regions, or even impede population convergence. To address the above issues, we propose a preference-driven surrogate-assisted evolutionary multiobjective feature selection algorithm, where three surrogate-based strategies are developed by introducing objective preferences. A surrogate-assisted initialization strategy is employed to initialize a set of high-quality feature subsets that encompass important features. Two surrogate-assisted evolutionary search strategies are tailored to effectively identify feature subsets with a small number of features and feature subsets with high classification performance with the assistance of single and multiple surrogate models, respectively. Moreover, the two surrogate-assisted evolutionary search strategies are combined with the full-evaluation-based search to further increase the classification performance. The proposed method has been tested on 23 diverse datasets. The experimental results demonstrate that the performance of the proposed algorithm outperforms that of six other recently developed methods under the same time budget.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-22"},"PeriodicalIF":4.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076596","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 Hybrid Evolutionary Algorithm for Time-Dependent Green Vehicle Routing Problem with Time Windows and Route Balancing in Cold Chain. 冷链中具有时间窗和路径平衡的时变绿色车辆路径问题的混合进化算法。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-09-18 DOI: 10.1162/EVCO.a.407

Jianhao Xu, Zhuang Yang, Yang Wang

{"title":"A Hybrid Evolutionary Algorithm for Time-Dependent Green Vehicle Routing Problem with Time Windows and Route Balancing in Cold Chain.","authors":"Jianhao Xu, Zhuang Yang, Yang Wang","doi":"10.1162/EVCO.a.407","DOIUrl":"https://doi.org/10.1162/EVCO.a.407","url":null,"abstract":"This paper focuses on the cold chain logistics and proposes a multi-objective Vehicle Routing Problem (VRP) model that seeks to minimize the total cost of cold chain logistics and maximize the fairness of employees' workloads. This model first incorporates carbon emissions into the cost structure, and also discretely calculates vehicle departure times by introducing time-dependent factors. Specifically, an algorithm named NSGA-ALNS is developed to solve the proposed model. Different from the well-known genetic algorithmic frameworks that include the selection, crossover, and mutation steps, the proposed algorithm utilizes the population evolution strategy of NSGA-II for global search, employs Adaptive Large Neighborhood Search (ALNS) as a local search operator for route refinement, and further introduces a population diversity strategy to reduce duplicated individuals. Empirically, comparing and analyzing the Pareto frontiers of NSGA-ALNS, NSGA-II, and MOALNS on the Solomon dataset and Gehring & Homberger's instances, we clearly demonstrate that NSGAALNS improves the convergence and the extent of the solution set.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-21"},"PeriodicalIF":4.1,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149018628","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

Learning Variable Importance and Interaction in Genetic Algorithms for Pseudo-Boolean Optimization. 伪布尔优化遗传算法中变量重要性和交互学习。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-09-15 DOI: 10.1162/EVCO.a.406

Renato Tinós, Francisco Chicano, Michal W Przewozniczek, Darrell Whitley

引用次数: 0

Generation of Continuous Optimization Benchmark Problems Guided by Deep Exploratory Landscape Analysis. 基于深度探索性景观分析的连续优化基准问题生成

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-09-15 DOI: 10.1162/EVCO.a.405

Konstantin Dietrich, Moritz Seiler, Olaf Mersmann, Heike Trautmann, Pascal Kerschke

{"title":"Generation of Continuous Optimization Benchmark Problems Guided by Deep Exploratory Landscape Analysis.","authors":"Konstantin Dietrich, Moritz Seiler, Olaf Mersmann, Heike Trautmann, Pascal Kerschke","doi":"10.1162/EVCO.a.405","DOIUrl":"https://doi.org/10.1162/EVCO.a.405","url":null,"abstract":"The development and evaluation of optimization algorithms critically rely on representative benchmark functions. However, existing benchmarks often offer limited coverage of the diverse landscape characteristics encountered in real-world problems. Moreover, real-world black-box optimization tasks involve costly or time-consuming evaluations, highlighting the need for efficient surrogate models. This work presents a novel, feature-driven method for the automated and targeted generation of synthetic benchmark functions using Deep Exploratory Landscape Analysis (Deep ELA) features. The generated functions are neural network models tailored such that their Deep ELA feature vectors closely match user-specified targets. Unlike previous approaches that depend on handcrafted ELA features and computationally expensive surrogate modeling, our method leverages pretrained transformer-based embeddings and gradient-based optimization to directly produce functions exhibiting desired feature combinations. Our method can generate functions whose Deep ELA vectors resemble those of BBOB problems across multiple dimensions, posing similar structural properties and yielding comparable algorithmic performances. Furthermore, our approach supports the generation of novel benchmark instances, with structural characteristics whose feature representations extend beyond those covered by existing test suites. Overall, our proposed approach offers a scalable methodology for creating efficient surrogate models of existing benchmark libraries or expensive real-world problems, and for generating novel optimization problems with much more diverse landscape characteristics.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-38"},"PeriodicalIF":4.1,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978603","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

Survey of Interactive Evolutionary Decomposition-Based Multiobjective Optimization Methods 基于交互进化分解的多目标优化方法综述。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-07-13 Epub Date: 2026-09-11 DOI: 10.1162/EVCO_a_00366

Giomara Lárraga;Kaisa Miettinen

{"title":"Survey of Interactive Evolutionary Decomposition-Based Multiobjective Optimization Methods","authors":"Giomara Lárraga;Kaisa Miettinen","doi":"10.1162/EVCO_a_00366","DOIUrl":"10.1162/EVCO_a_00366","url":null,"abstract":"Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature. In the evolutionary computation community, the so-called decomposition-based methods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple subproblems to be solved collaboratively. Various interactive versions of decomposition-based methods have been proposed. However, most of them do not consider the desirable properties of real interactive solution processes, such as avoiding imposing a high cognitive burden on the decision-maker, allowing them to decide when to interact with the method, and supporting them in selecting a final solution. This article reviews interactive evolutionary decomposition-based multiobjective optimization methods and different methodologies utilized to incorporate interactivity in them. Additionally, desirable properties of interactive decomposition-based multiobjective evolutionary optimization methods are identified, aiming to make them easier to be applied in real-world problems.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"34 3","pages":"261-305"},"PeriodicalIF":4.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015551","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

Beyond Landscape Analysis: DynamoRep Features for Capturing Algorithm-Problem Interaction in Single-Objective Continuous Optimization 超越景观分析：单目标连续优化中捕捉算法问题交互的DynamoRep特征。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-07-13 Epub Date: 2026-09-11 DOI: 10.1162/EVCO_a_00370

Gjorgjina Cenikj;Gašper Petelin;Carola Doerr;Peter Korošec;Tome Eftimov

{"title":"Beyond Landscape Analysis: DynamoRep Features for Capturing Algorithm-Problem Interaction in Single-Objective Continuous Optimization","authors":"Gjorgjina Cenikj;Gašper Petelin;Carola Doerr;Peter Korošec;Tome Eftimov","doi":"10.1162/EVCO_a_00370","DOIUrl":"10.1162/EVCO_a_00370","url":null,"abstract":"The representation of optimization problems and algorithms in terms of numerical features is a well-established tool for comparing optimization problem instances, for analyzing the behavior of optimization algorithms, and the quality of existing problem benchmarks, as well as for automated per-instance algorithm selection and configuration approaches. Extending purely problem-centered feature collections, our recently proposed DynamoRep features provide a simple and inexpensive representation of the algorithm-problem interaction during the optimization process. In this paper, we conduct a comprehensive analysis of the predictive power of the DynamoRep features for the problem classification, algorithm selection, and algorithm classification tasks. In particular, the features are evaluated for the classification of problem instances into problem classes from the BBOB (Black Box Optimization Benchmarking) suite, selecting the best algorithm to solve a given problem from a portfolio of three algorithms (Differential Evolution, Evolutionary Strategy, and Particle Swarm Optimization), as well as distinguishing these algorithms based on their trajectories. We show that, despite being much cheaper to compute, they can yield results comparable to those using state-of-the-art Exploratory Landscape Analysis features.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"34 3","pages":"341-366"},"PeriodicalIF":4.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575999","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

Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems* 多目标优化问题MOEA/D典型分解方法的运行时分析

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-07-13 Epub Date: 2026-09-11 DOI: 10.1162/EVCO_a_00364

Zhengxin Huang;Yuren Zhou;Zefeng Chen;Qianlong Dang

{"title":"Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems*","authors":"Zhengxin Huang;Yuren Zhou;Zefeng Chen;Qianlong Dang","doi":"10.1162/EVCO_a_00364","DOIUrl":"10.1162/EVCO_a_00364","url":null,"abstract":"Decomposition-based multiobjective evolutionary algorithms (MOEAs) are popular methods utilized to address many-objective optimization problems (MaOPs). These algorithms decompose the original MaOP into several scalar optimization subproblems, and solve them to obtain a set of solutions to approximate the Pareto front (PF). The decomposition approach is an important component in them. This paper presents a runtime analysis of a MOEA based on the classic decomposition framework using the typical weighted sum (WS), Tchebycheff (TCH), and penalty-based boundary intersection (PBI) approaches to obtain an optimal solution for any subproblem of two pseudo-Boolean benchmark MaOPs, namely mLOTZ and mCOCZ. Due to the complexity and limitation of the theoretical analysis techniques, the analyzed algorithm employs 1-bit mutation to generate offspring individuals. The results indicate that when using WS, the analyzed algorithm can consistently find an optimal solution for every subproblem, which is located in the PF, in polynomial expected runtime. In contrast, the algorithm requires at least exponential expected runtime (with respect to the number of objectives m) for certain subproblems when using TCH or PBI, even though the landscapes of all objective functions in the two benchmarks are strictly monotone. Moreover, this analysis reveals a drawback of using WS: the optimal solutions obtained by solving subproblems are more easily mapped to the same point in the PF, compared to the case of using TCH. When using PBI, a smaller value of the penalty parameter is a good choice for faster convergence to the PF but may compromise diversity. To further understand the impact of these approaches in practical algorithms, numerical experiments on using bit-wise mutation to generate offspring individuals are conducted. The findings of this study may be helpful for designing more efficient decomposition approaches for MOEAs in future research.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"34 3","pages":"307-339"},"PeriodicalIF":4.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015549","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

Quality Diversity under Sparse Interaction and Sparse Reward: Application to Grasping in Robotics 稀疏交互和稀疏奖励下的质量多样性：在机器人抓取中的应用。

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-07-13 Epub Date: 2026-09-11 DOI: 10.1162/EVCO_a_00363

Johann Huber;François Helenon;Miranda Coninx;Faïz Ben Amar;Stéphane Doncieux

{"title":"Quality Diversity under Sparse Interaction and Sparse Reward: Application to Grasping in Robotics","authors":"Johann Huber;François Helenon;Miranda Coninx;Faïz Ben Amar;Stéphane Doncieux","doi":"10.1162/EVCO_a_00363","DOIUrl":"10.1162/EVCO_a_00363","url":null,"abstract":"Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-performing solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains—mainly applied to locomotion, where the fitness and the behavior signal are dense. Grasping is a crucial task for manipulation in robotics. Despite the efforts of many research communities, this task is yet to be solved. Grasping cumulates unprecedented challenges in QD literature: it suffers from reward sparsity, behavioral sparsity, and behavior space misalignment. The present work studies how QD can address grasping. Experiments have been conducted on 15 different methods on 10 grasping domains, corresponding to two different robot-gripper setups and five standard objects. The obtained results show that MAP-Elites variants that select successful solutions in priority outperform all the compared methods on the studied metrics by a large margin. We also found experimental evidence that sparse interaction can lead to deceptive novelty. To our knowledge, the ability to efficiently produce examples of grasping trajectories demonstrated in this work has no precedent in the literature.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"34 3","pages":"367-397"},"PeriodicalIF":4.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015468","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-Based Feature Selection for Symbolic Regression on Incomplete Data 基于遗传编程的不完整数据符号回归特征选择

IF 4.1 2区计算机科学

Evolutionary Computation Pub Date : 2026-07-13 Epub Date: 2026-09-11 DOI: 10.1162/EVCO_a_00362

Baligh Al-Helali;Qi Chen;Bing Xue;Mengjie Zhang

{"title":"Genetic Programming-Based Feature Selection for Symbolic Regression on Incomplete Data","authors":"Baligh Al-Helali;Qi Chen;Bing Xue;Mengjie Zhang","doi":"10.1162/EVCO_a_00362","DOIUrl":"10.1162/EVCO_a_00362","url":null,"abstract":"High-dimensionality is one of the serious real-world data challenges in symbolic regression and it is more challenging if the data are incomplete. Genetic programming has been successfully utilised for high-dimensional tasks due to its natural feature selection ability, but it is not directly applicable to incomplete data. Commonly, it needs to impute the missing values first and then perform genetic programming on the imputed complete data. However, in the case of having many irrelevant features being incomplete, intuitively, it is not necessary to perform costly imputations on such features. For this purpose, this work proposes a genetic programming-based approach to select features directly from incomplete high-dimensional data to improve symbolic regression performance. We extend the concept of identity/neutral elements from mathematics into the function operators of genetic programming, thus they can handle the missing values in incomplete data. Experiments have been conducted on a number of data sets considering different missingness ratios in high-dimensional symbolic regression tasks. The results show that the proposed method leads to better symbolic regression results when compared with state-of-the-art methods that can select features directly from incomplete data. Further results show that our approach not only leads to better symbolic regression accuracy but also selects a smaller number of relevant features, and consequently improves both the effectiveness and the efficiency of the learning process.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"34 3","pages":"399-426"},"PeriodicalIF":4.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689431","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

Computing Optimal Populations for Binary Problems using Logic Minimization. 用逻辑最小化计算二元问题的最优种群。

IF 3.4 2区计算机科学

Evolutionary Computation Pub Date : 2026-05-08 DOI: 10.1162/EVCO.a.399

Pier Luca Lanzi

{"title":"Computing Optimal Populations for Binary Problems using Logic Minimization.","authors":"Pier Luca Lanzi","doi":"10.1162/EVCO.a.399","DOIUrl":"https://doi.org/10.1162/EVCO.a.399","url":null,"abstract":"The study of generalization in XCS has been mainly focused on single-step binary problems for which the optimal (accurate, maximally general) solution is known. In contrast, binary multi-step problems have primarily been studied in terms of performance. However, while there is an intuitive notion of generalization in multi-step environments, the optimal solutions for even the simplest multi-step problems are still unknown. This paper presents an approach to compute the optimal solutions for single-step and multi-step binary problems starting from their tabular solution. We first illustrate the approach using Boolean functions for which the optimal solutions are known. Then, we apply it to compute, for the first time, the optimal solutions for theWoods problems that have been used as a testbed to study XCS behavior in multi-step problems. The solutions confirm early intuitions on simple environments and shed new light on more complex problems. We compare the optimal solutions our approach computes with the condensed populations that XCS can evolve, showing that XCS consistently evolves a number of minimal solutions that increases with the number of learning problems. Finally, we extend our approach to compute the minimal representation of evolving classifier populations and compare the size of the evolved populations before and after condensation with their minimized counterparts.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-23"},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845553","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