Evolutionary Computation最新文献

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

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context 进化黑箱背景下的代用模型景观分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1162/evco_a_00357

Zbyněk Pitra;Jan Koza;Jiří Tumpach;Martin Holeňa

引用次数: 0

Evolutionary Sparsity Regularisation-Based Feature Selection for Binary Classification 基于进化稀疏正则化的二元分类特征选择

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1162/evco_a_00358

Bach Hoai Nguyen;Bing Xue;Mengjie Zhang

引用次数: 0

Runtime Analysis of Single- and Multiobjective Evolutionary Algorithms for Chance-Constrained Optimization Problems with Normally Distributed Random Variables* 针对具有正态分布随机变量的机会约束优化问题的单目标和多目标进化算法的运行时间分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-06-02 DOI: 10.1162/evco_a_00355

Frank Neumann;Carsten Witt

引用次数: 0

Analysis and simplification of the winner of the CEC 2022 optimization competition on single objective bound constrained search. CEC 2022优化竞赛中单目标有界约束搜索优胜者的分析与简化。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1162/evco.a.27

Rafał Biedrzycki

{"title":"Analysis and simplification of the winner of the CEC 2022 optimization competition on single objective bound constrained search.","authors":"Rafał Biedrzycki","doi":"10.1162/evco.a.27","DOIUrl":"https://doi.org/10.1162/evco.a.27","url":null,"abstract":"Extending state-of-the-art evolutionary algorithms is a widespread research direction. This trend has resulted in algorithms that give good results but are complex and challenging to analyze. One of these algorithms is EA4Eig - the winner of the CEC 2022 competition on single objective bound constrained search. The algorithm internally uses four optimization algorithms with modified components. This paper presents an analysis of EA4Eig and proposes a simplified version thereof exhibiting better optimization performance. The analysis found that the original source code contains errors that impact the algorithm's rank. The code was corrected, and the CEC 2022 competition ranking was recalculated. The impact of individual EA4Eig components on its performance was empirically analyzed. As a result, the algorithm was simplified by removing two of them. The best remaining component was analyzed further, which made it possible to remove some unnecessary and harmful code. Several versions of the algorithm were created and tested, varying in the degree of simplification. The simplest of them is implemented in 244 lines of C++ code, whereas the original implementation used 716 lines of Matlab code. Further analyses focused on the parameters of the algorithm. The constants hidden in the source code were named and treated as additional configurable parameters that underwent tuning. The ablation analyses showed that two of these hidden parameters had the most significant impact on the improvement achieved by the tuned version. The results of the original and simplified versions were compared on CEC 2022 and BBOB benchmarks. The results confirm that the simplified version is better than the original one on both these benchmarks.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-19"},"PeriodicalIF":4.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081813","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

Cross-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations. 交叉表示遗传规划：基于树和线性表示的案例研究。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1162/evco.a.25

Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf

{"title":"Cross-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations.","authors":"Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang, Wolfgang Banzhaf","doi":"10.1162/evco.a.25","DOIUrl":"https://doi.org/10.1162/evco.a.25","url":null,"abstract":"Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various pros and cons in different domains. However, due to the complicated relationships among representation and fitness landscapes of GP, it is hard to intuitively determine which GP representation is the most suitable for solving a certain problem. Evolving programs (or models) with multiple representations simultaneously can alternatively search on different fitness landscapes since representations are highly related to the search space that essentially defines the fitness landscape. Fully using the latent synergies among different GP individual representations might be helpful for GP to search for better solutions. However, existing GP literature rarely investigates the simultaneous effective evolution of multiple representations. To fill this gap, this paper proposes a cross-representation GP algorithm based on tree-based and linear representations, which are two commonly used GP representations. In addition, we develop a new cross-representation crossover operator to harness the interplay between tree-based and linear representations. Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP with solely tree-based or linear representation in solving symbolic regression and dynamic job shop scheduling problems.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-28"},"PeriodicalIF":4.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081814","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 stochastic operators, fitness landscapes, and optimization heuristics performances. 关于随机算子，适应度景观和优化启发式性能。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1162/evco.a.24

Brahim Aboutaib, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, Belaïd Ahiod

{"title":"On stochastic operators, fitness landscapes, and optimization heuristics performances.","authors":"Brahim Aboutaib, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, Belaïd Ahiod","doi":"10.1162/evco.a.24","DOIUrl":"https://doi.org/10.1162/evco.a.24","url":null,"abstract":"Stochastic operators are the backbone of many stochastic optimization algorithms. Besides the existing theoretical analysis that analyzes the asymptotic runtime of such algorithms, characterizing their performances using fitness landscapes analysis is far away. The fitness landscape approach proceeds by considering multiple characteristics to understand and explain an optimization algorithm's performance or the difficulty of an optimization problem, and hence would provide a richer explanation. This paper analyzes the fitness landscapes of stochastic operators by focusing on the number of local optima and their relation to the optimization performance. The search spaces of two combinatorial problems are studied, the NK-landscape and the Quadratic Assignment Problem, using binary string-based and permutation-based stochastic operators. The classical bit-flip search operator is considered for binary strings, and a generalization of the deterministic exchange operator for permutation representations is devised. We study small instances, ranging from randomly generated to real-like instances, and large instances from the NK-landscapes. For large instances, we propose using an adaptive walk process to estimate the number of locally optimal solutions. Given that stochastic operators are usually used within the population and single solution-based evolutionary optimization algorithms, we contrasted the performances of the (μ + λ)-EA, and an Iterated Local Search, versus the landscape properties of large size instances of the NK-landscapes. Our analysis shows that characterizing the fitness landscapes induced by stochastic search operators can effectively explain the optimization performances of the algorithms we considered.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-27"},"PeriodicalIF":4.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081816","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 with Tabu List for Dynamic Flexible Job Shop Scheduling. 基于禁忌列表的柔性作业车间动态调度遗传规划。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-05-12 DOI: 10.1162/evco.a.26

Fangfang Zhang, Mazhar Ansari Ardeh, Yi Mei, Mengjie Zhang

{"title":"Genetic Programming with Tabu List for Dynamic Flexible Job Shop Scheduling.","authors":"Fangfang Zhang, Mazhar Ansari Ardeh, Yi Mei, Mengjie Zhang","doi":"10.1162/evco.a.26","DOIUrl":"https://doi.org/10.1162/evco.a.26","url":null,"abstract":"Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem, requiring simultaneous decision-making for machine assignment and operation sequencing in dynamic environments. Genetic programming (GP), as a hyper-heuristic approach, has been extensively employed for acquiring scheduling heuristics for DFJSS. A drawback of GP for DFJSS is that GP has weak exploration ability indicated by its quick diversity loss during the evolutionary process. This paper proposes an effective GP algorithm with tabu lists to capture the information of explored areas and guide GP to explore more unexplored areas to improve GP's exploration ability for enhancing GP's effectiveness. First, we use phenotypic characterisation to represent the behaviour of tree-based GP individuals for DFJSS as vectors. Then, we build tabu lists that contain phenotypic characterisations of explored individuals at the current generation and across generations, respectively. Finally, newly generated offspring are compared with the individuals' phenotypic characterisations in the built tabu lists. If an individual is unseen in the tabu lists, it will be kept to form the new population at the next generation. Otherwise, it will be discarded. We have examined the proposed GP algorithm in nine different scenarios. The findings indicate that the proposed algorithm outperforms the compared algorithms in the majority of scenarios. The proposed algorithm can maintain a diverse and well-distributed population during the evolutionary process of GP. Further analyses show that the proposed algorithm does cover a large search area to find effective scheduling heuristics by focusing on unseen individuals.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-29"},"PeriodicalIF":4.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081815","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