Evolutionary Computation最新文献_第10页

The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis 单变量边际分布算法能很好地处理欺骗和溢出

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-01 DOI: 10.1162/evco_a_00293

Benjamin Doerr;Martin S. Krejca

{"title":"The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis","authors":"Benjamin Doerr;Martin S. Krejca","doi":"10.1162/evco_a_00293","DOIUrl":"10.1162/evco_a_00293","url":null,"abstract":"In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by the choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most λ(n2+2elnn) fitness evaluations. Since an offspring population size λ of order nlogn can prevent genetic drift, the UMDA can solve the DLB problem with O(n2logn) fitness evaluations. In contrast, for classic evolutionary algorithms no better runtime guarantee than O(n3) is known (which we prove to be tight for the (1+1) EA), so our result rather suggests that the UMDA can cope well with deception and epistatis. From a broader perspective, our result shows that the UMDA can cope better with local optima than many classic evolutionary algorithms; such a result was previously known only for the compact genetic algorithm. Together with the lower bound of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 4","pages":"543-563"},"PeriodicalIF":6.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39499751","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: 24

Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions 多目标进化算法仍然很好：最大化单调近似子模负模函数

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-01 DOI: 10.1162/evco_a_00288

Chao Qian

{"title":"Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions","authors":"Chao Qian","doi":"10.1162/evco_a_00288","DOIUrl":"10.1162/evco_a_00288","url":null,"abstract":"As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical explanation of the behavior of EAs. Particularly, a simple multiobjective EA, that is, GSEMO, has been shown to be able to achieve good polynomial-time approximation guarantees for submodular optimization, where the objective function is only required to satisfy some properties and its explicit formulation is not needed. Submodular optimization has wide applications in diverse areas, and previous studies have considered the cases where the objective functions are monotone submodular, monotone non-submodular, or non-monotone submodular. To complement this line of research, this article studies the problem class of maximizing monotone approximately submodular minus modular functions (i.e., g-c) with a size constraint, where g is a so-called non-negative monotone approximately submodular function and c is a so-called non-negative modular function, resulting in the objective function (g-c) being non-monotone non-submodular in general. Different from previous analyses, we prove that by optimizing the original objective function (g-c) and the size simultaneously, the GSEMO fails to achieve a good polynomial-time approximation guarantee. However, we also prove that by optimizing a distorted objective function and the size simultaneously, the GSEMO can still achieve the best-known polynomial-time approximation guarantee. Empirical studies on the applications of Bayesian experimental design and directed vertex cover show the excellent performance of the GSEMO.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 4","pages":"463-490"},"PeriodicalIF":6.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39089247","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: 13

Automatically Evolving Texture Image Descriptors Using the Multitree Representation in Genetic Programming Using Few Instances. 基于少实例遗传规划的纹理图像描述符多树自动进化。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-09-01 DOI: 10.1162/evco_a_00284

Harith Al-Sahaf, Ausama Al-Sahaf, Bing Xue, Mengjie Zhang

{"title":"Automatically Evolving Texture Image Descriptors Using the Multitree Representation in Genetic Programming Using Few Instances.","authors":"Harith Al-Sahaf, Ausama Al-Sahaf, Bing Xue, Mengjie Zhang","doi":"10.1162/evco_a_00284","DOIUrl":"https://doi.org/10.1162/evco_a_00284","url":null,"abstract":"The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, for example, corners, line-segments, and shapes, in an image and extracting features from those keypoints. In this article, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multitree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six handcrafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"331-366"},"PeriodicalIF":6.8,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38639796","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: 1

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. 环境变化下自主学习的进化可塑性。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-09-01 DOI: 10.1162/evco_a_00286

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy

{"title":"Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions.","authors":"Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy","doi":"10.1162/evco_a_00286","DOIUrl":"https://doi.org/10.1162/evco_a_00286","url":null,"abstract":"A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 3","pages":"391-414"},"PeriodicalIF":6.8,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9172052","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: 11

Interaction-Transformation Evolutionary Algorithm for Symbolic Regression. 符号回归的交互变换进化算法。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-09-01 DOI: 10.1162/evco_a_00285

F O de Franca, G S I Aldeia

{"title":"Interaction-Transformation Evolutionary Algorithm for Symbolic Regression.","authors":"F O de Franca, G S I Aldeia","doi":"10.1162/evco_a_00285","DOIUrl":"https://doi.org/10.1162/evco_a_00285","url":null,"abstract":"Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This article introduces a mutation-only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear, and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art nonlinear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"367-390"},"PeriodicalIF":6.8,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38701498","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: 31

Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine. 一台机器上具有恒定处理时间的缓冲双机排列流车间的迭代局部搜索及其他算法。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-09-01 DOI: 10.1162/evco_a_00287

Hoang Thanh Le, Philine Geser, Martin Middendorf

{"title":"Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine.","authors":"Hoang Thanh Le, Philine Geser, Martin Middendorf","doi":"10.1162/evco_a_00287","DOIUrl":"https://doi.org/10.1162/evco_a_00287","url":null,"abstract":"The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various applications, for example, when one machine is a packing machine or when materials have to be transported. Different types of buffers and buffer usage are considered. It is shown that all considered buffer flow shop problems remain NP-hard for the makespan criterion even with the restriction to equal processing times on one machine. However, the special case where the constant c is larger or smaller than all processing times on the other machine is shown to be polynomially solvable by presenting an algorithm (2BF-OPT) that calculates optimal schedules in O(nlogn) steps. Two heuristics for solving the NP-hard flow shop problems are proposed: (i) a modification of the commonly used NEH heuristic (mNEH) and (ii) an Iterated Local Search heuristic (2BF-ILS) that uses the mNEH heuristic for computing its initial solution. It is shown experimentally that the proposed 2BF-ILS heuristic obtains better results than two state-of-the-art algorithms for buffered flow shop problems from the literature and an Ant Colony Optimization algorithm. In addition, it is shown experimentally that 2BF-ILS obtains the same solution quality as the standard NEH heuristic, however, with a smaller number of function evaluations.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 3","pages":"415-439"},"PeriodicalIF":6.8,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9172054","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: 3

Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift. 基于负乘法漂移的非精英进化算法的下界。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00283

Benjamin Doerr

{"title":"Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift.","authors":"Benjamin Doerr","doi":"10.1162/evco_a_00283","DOIUrl":"https://doi.org/10.1162/evco_a_00283","url":null,"abstract":"A decent number of lower bounds for non-elitist population-based evolutionary algorithms has been shown by now. Most of them are technically demanding due to the (hard to avoid) use of negative drift theorems-general results which translate an expected movement away from the target into a high hitting time. We propose a simple negative drift theorem for multiplicative drift scenarios and show that it can simplify existing analyses. We discuss in more detail Lehre's (2010) negative drift in populations method, one of the most general tools to prove lower bounds on the runtime of non-elitist mutation-based evolutionary algorithms for discrete search spaces. Together with other arguments, we obtain an alternative and simpler proof of this result, which also strengthens and simplifies this method. In particular, now only three of the five technical conditions of the previous result have to be verified. The lower bounds we obtain are explicit instead of only asymptotic. This allows us to compute concrete lower bounds for concrete algorithms, but also enables us to show that super-polynomial runtimes appear already when the reproduction rate is only a (1-ω(n-1/2)) factor below the threshold. For the special case of algorithms using standard bit mutation with a random mutation rate (called uniform mixing in the language of hyper-heuristics), we prove the result stated by Dang and Lehre (2016b) and extend it to mutation rates other than Θ(1/n), which includes the heavy-tailed mutation operator proposed by Doerr et al. (2017). We finally use our method and a novel domination argument to show an exponential lower bound for the runtime of the mutation-only simple genetic algorithm on OneMax for arbitrary population size.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"305-329"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38616984","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: 14

Offline Learning with a Selection Hyper-Heuristic: An Application to Water Distribution Network Optimisation. 具有选择超启发式的离线学习:在配水网络优化中的应用。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00277

William B Yates, Edward C Keedwell

引用次数: 3

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. 基于模型的小表达式符号回归遗传规划改进。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00278

M Virgolin, T Alderliesten, C Witteveen, P A N Bosman

{"title":"Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions.","authors":"M Virgolin, T Alderliesten, C Witteveen, P A N Bosman","doi":"10.1162/evco_a_00278","DOIUrl":"https://doi.org/10.1162/evco_a_00278","url":null,"abstract":"The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"211-237"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/evco_a_00278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38075060","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: 49

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming. 基于语法的遗传规划中的概率上下文和结构依赖学习。

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-06-01 DOI: 10.1162/evco_a_00280

Pak-Kan Wong, Man-Leung Wong, Kwong-Sak Leung

{"title":"Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.","authors":"Pak-Kan Wong, Man-Leung Wong, Kwong-Sak Leung","doi":"10.1162/evco_a_00280","DOIUrl":"https://doi.org/10.1162/evco_a_00280","url":null,"abstract":"Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create suboptimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This article presents Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 2","pages":"239-268"},"PeriodicalIF":6.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38483671","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