Evolutionary Computation最新文献

P-NP instance decomposition based on the Fourier transform for solving the Linear Ordering Problem.

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-02-20 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":"https://doi.org/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 meta-heuristic 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 meta-heuristic 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 hyper-parameters.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-28"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","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

On the use of the Doubly Stochastic Matrix models for the Quadratic Assignment Problem.

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-02-20 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":"https://doi.org/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 article, 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 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 article 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":"1-30"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","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

Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems.

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-02-04 DOI: 10.1162/evco_a_00367

Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann

{"title":"Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems.","authors":"Moritz Vinzent Seiler, Pascal Kerschke, Heike Trautmann","doi":"10.1162/evco_a_00367","DOIUrl":"https://doi.org/10.1162/evco_a_00367","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 multi-objective optimization problems. Our proposed framework can either be used out-of-the-box for analyzing single- and multiobjective 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":"1-27"},"PeriodicalIF":4.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191085","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 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-01-14 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":"https://doi.org/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-basedmethods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple sub-problems 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 paper 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":" ","pages":"1-39"},"PeriodicalIF":4.6,"publicationDate":"2025-01-14","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

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 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-01-14 DOI: 10.1162/evco_a_00364

Zhengxin Huang, Yunren Zhou, Zefeng Chen, Qianlong Dang

{"title":"Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems.","authors":"Zhengxin Huang, Yunren Zhou, Zefeng Chen, Qianlong Dang","doi":"10.1162/evco_a_00364","DOIUrl":"https://doi.org/10.1162/evco_a_00364","url":null,"abstract":"Decomposition-based multi-objective 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 one-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":" ","pages":"1-32"},"PeriodicalIF":4.6,"publicationDate":"2025-01-14","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 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-01-14 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":"https://doi.org/10.1162/evco_a_00363","url":null,"abstract":"Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and highperforming 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 2 different robot-gripper setups and 5 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":" ","pages":"1-30"},"PeriodicalIF":4.6,"publicationDate":"2025-01-14","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

The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits. 进化算法中随机性的代价：交叉可以节省随机比特。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2025-01-14 DOI: 10.1162/evco_a_00365

Carlo Kneissl, Dirk Sudholt

{"title":"The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits.","authors":"Carlo Kneissl, Dirk Sudholt","doi":"10.1162/evco_a_00365","DOIUrl":"https://doi.org/10.1162/evco_a_00365","url":null,"abstract":"Evolutionary algorithms make countless random decisions during selection, mutation and crossover operations. These random decisions require a steady stream of random numbers. We analyze the expected number of random bits used throughout a run of an evolutionary algorithm and refer to this as the cost of randomness. We give general bounds on the cost of randomness for mutation-based evolutionary algorithms using 1-bit flips or standard mutations using either a naive or a common, more efficient implementation that uses Θ(logn) random bits per mutation. Uniform crossover is a potentially wasteful operator as the number of random bits used equals the Hamming distance of the two parents, which can be up to n. However, we show for a (2+1) Genetic Algorithm that is known to optimize the test function ONEMAX in roughly (e/2)nlnn expected evaluations, twice as fast as the fastest mutation-based evolutionary algorithms, that the total cost of randomness during all crossover operations on ONEMAX is only Θ(n). A more pronounced effect is shown for the common test function JUMPk, where there is an asymptotic decrease both in the number of evaluations and in the cost of randomness. Consequently, the use of crossover can reduce the cost of randomness below that of the fastest evolutionary algorithms that only use standard mutations.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-29"},"PeriodicalIF":4.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015553","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

Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving 知情下采样词库选择：为高效解决问题识别富有成效的训练案例。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-12-02 DOI: 10.1162/evco_a_00346

Ryan Boldi;Martin Briesch;Dominik Sobania;Alexander Lalejini;Thomas Helmuth;Franz Rothlauf;Charles Ofria;Lee Spector

{"title":"Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving","authors":"Ryan Boldi;Martin Briesch;Dominik Sobania;Alexander Lalejini;Thomas Helmuth;Franz Rothlauf;Charles Ofria;Lee Spector","doi":"10.1162/evco_a_00346","DOIUrl":"10.1162/evco_a_00346","url":null,"abstract":"Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases, allowing for more individuals to be explored with the same number of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 4","pages":"307-337"},"PeriodicalIF":4.6,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562620","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

Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming 语法引导遗传编程的分布算法估算。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-12-02 DOI: 10.1162/evco_a_00345

Pablo Ramos Criado;D. Barrios Rolanía;David de la Hoz;Daniel Manrique

{"title":"Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming","authors":"Pablo Ramos Criado;D. Barrios Rolanía;David de la Hoz;Daniel Manrique","doi":"10.1162/evco_a_00345","DOIUrl":"10.1162/evco_a_00345","url":null,"abstract":"Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This paper presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"32 4","pages":"339-370"},"PeriodicalIF":4.6,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139565374","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

Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function 区域差分元进化：一种求多变量函数所有理想最优的算法。

IF 4.6 2区计算机科学

Evolutionary Computation Pub Date : 2024-12-02 DOI: 10.1162/evco_a_00337

Richard Wehr;Scott R. Saleska

引用次数: 0