Evolutionary Computation最新文献

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

BlindSMOTE: Synthetic minority oversampling based only on evolutionary computation. 盲击：仅基于进化计算的合成少数过采样。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-04-16 DOI: 10.1162/evco_a_00374

Nicolás E Garcí-Pedrajas, José M Cuevas-Muñoz, Aida de Haro-García

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 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-03-15 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":"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":"33 1","pages":"91-114"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","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

OneMax Is Not the Easiest Function for Fitness Improvements OneMax 并非改善体能的最简单功能。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-03-15 DOI: 10.1162/evco_a_00348

Marc Kaufmann;Maxime Larcher;Johannes Lengler;Xun Zou

引用次数: 0

Synthesising Diverse and Discriminatory Sets of Instances Using Novelty Search in Combinatorial Domains 在组合领域利用新颖性搜索合成多样化和辨别性实例集。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-03-15 DOI: 10.1162/evco_a_00350

Alejandro Marrero;Eduardo Segredo;Coromoto León;Emma Hart

{"title":"Synthesising Diverse and Discriminatory Sets of Instances Using Novelty Search in Combinatorial Domains","authors":"Alejandro Marrero;Eduardo Segredo;Coromoto León;Emma Hart","doi":"10.1162/evco_a_00350","DOIUrl":"10.1162/evco_a_00350","url":null,"abstract":"Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the “performance gap” between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics, and state-of-the-art methods), thereby providing further evidence of its generality.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"33 1","pages":"55-90"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877841","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

Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms 精英进化算法的漂移分析与适合度分析

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-03-15 DOI: 10.1162/evco_a_00349

Jun He;Yuren Zhou

{"title":"Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms","authors":"Jun He;Yuren Zhou","doi":"10.1162/evco_a_00349","DOIUrl":"10.1162/evco_a_00349","url":null,"abstract":"The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multiobjective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"33 1","pages":"1-25"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140295207","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 Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems 布尔问题分类器学习系统中的分层学习扩展方法。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-03-15 DOI: 10.1162/evco_a_00351

Isidro M. Alvarez;Trung B. Nguyen;Will N. Browne;Mengjie Zhang

{"title":"A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems","authors":"Isidro M. Alvarez;Trung B. Nguyen;Will N. Browne;Mengjie Zhang","doi":"10.1162/evco_a_00351","DOIUrl":"10.1162/evco_a_00351","url":null,"abstract":"Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality), and then successfully reuse them in larger-scale and/or related problems. Linking solutions to problems has been achieved through layered learning, where an experimenter sets a series of simpler related problems to solve a more complex task. Recent works on Learning Classifier Systems (LCSs) has shown that knowledge reuse through the adoption of Code Fragments, GP-like tree-based programs, is plausible. However, random reuse is inefficient. Thus, the research question is how LCS can adopt a layered-learning framework, such that increasingly complex problems can be solved efficiently. An LCS (named XCSCF*) has been developed to include the required base axioms necessary for learning, refined methods for transfer learning and learning recast as a decomposition into a series of subordinate problems. These subordinate problems can be set as a curriculum by a teacher, but this does not mean that an agent can learn from it; especially if it only extracts over-fitted knowledge of each problem rather than the underlying scalable patterns and functions. Results show that from a conventional tabula rasa, with only a vague notion of which subordinate problems might be relevant, XCSCF* captures the general logic behind the tested domains and therefore can solve any n-bit Multiplexer, n-bit Carry-one, n-bit Majority-on, and n-bit Even-parity problems. This work demonstrates a step towards continual learning as learned knowledge is effectively reused in subsequent problems.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"33 1","pages":"115-140"},"PeriodicalIF":4.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877840","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