Evolutionary Computation最新文献_第3页

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 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-05-06 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":"https://doi.org/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 together 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 what 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":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-05-06","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

OneMax is not the Easiest Function for Fitness Improvements. OneMax 并非改善体能的最简单功能。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-22 DOI: 10.1162/evco_a_00348

Marc Kaufmann, Maxime Larcher, Johannes Lengler, Xun Zou

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 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-22 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":"https://doi.org/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 multi-objective 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":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-03-22","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

Editorial for the Special Issue on Reproducibility. 可重复性特刊编辑。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_e_00344

Manuel López-Ibáñez, Luís Paquete, Mike Preuss

引用次数: 0

A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem. 可重复实验的实用方法：双排设施布局问题的应用。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_a_00317

Raúl Martín-Santamaría, Sergio Cavero, Alberto Herrán, Abraham Duarte, J Manuel Colmenar

引用次数: 0

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. 受约束的重要性：处理微分进化论中的不可行解及其他问题

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_a_00333

Anna V Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A Mitran, Daniela Zaharie

{"title":"The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond.","authors":"Anna V Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A Mitran, Daniela Zaharie","doi":"10.1162/evco_a_00333","DOIUrl":"10.1162/evco_a_00333","url":null,"abstract":"We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours in terms of performance, disruptiveness, and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants, on a special test function and the BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem dimensionality. Differential Evolution is not at all special in this regard-there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the heuristic optimisation community to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we refer to as the strategy of dealing with infeasible solutions. This component needs to be consistently: (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on an algorithm's performance in a wider sense (i.e., convergence time, robustness, etc.), and (c) included in the (automatic) design of algorithms. All of these should be done even for problems with bound constraints.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9474478","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

Using Decomposed Error for Reproducing Implicit Understanding of Algorithms. 利用分解错误重现对算法的隐性理解。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-03-01 DOI: 10.1162/evco_a_00321

Caitlin A Owen, Grant Dick, Peter A Whigham

{"title":"Using Decomposed Error for Reproducing Implicit Understanding of Algorithms.","authors":"Caitlin A Owen, Grant Dick, Peter A Whigham","doi":"10.1162/evco_a_00321","DOIUrl":"10.1162/evco_a_00321","url":null,"abstract":"Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly, multiple runs of an algorithm, without a fixed random seed, should ideally return statistically equivalent results. Secondly, it should be confirmed whether the expected behaviour of an algorithm matches its actual behaviour, in terms of how an algorithm targets a reduction in prediction error. Confirming the behaviour of an algorithm is not possible when using a total error aggregate score. Using an error decomposition framework as a methodology for improving the reproducibility of results in evolutionary computation addresses both of these factors. By estimating decomposed error using multiple runs of an algorithm and multiple training sets, the framework provides a greater degree of certainty about the prediction error. Also, decomposing error into bias, variance due to the algorithm (internal variance), and variance due to the training data (external variance) more fully characterises evolutionary algorithms. This allows the behaviour of an algorithm to be confirmed. Applying the framework to a number of evolutionary algorithms shows that their expected behaviour can be different to their actual behaviour. Identifying a behaviour mismatch is important in terms of understanding how to further refine an algorithm as well as how to effectively apply an algorithm to a problem.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9084698","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

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 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-02-19 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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-02-19","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

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 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-01-26 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":"https://doi.org/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 amount 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":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-01-26","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 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2024-01-26 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":"https://doi.org/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 article 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":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-01-26","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