Evolutionary Computation最新文献_第8页

Modular Grammatical Evolution for the Generation of Artificial Neural Networks 人工神经网络生成的模块化语法演化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-08 DOI: 10.1145/3520304.3534072

Khabat Soltanian, Ali Ebnenasir, M. Afsharchi

{"title":"Modular Grammatical Evolution for the Generation of Artificial Neural Networks","authors":"Khabat Soltanian, Ali Ebnenasir, M. Afsharchi","doi":"10.1145/3520304.3534072","DOIUrl":"https://doi.org/10.1145/3520304.3534072","url":null,"abstract":"Abstract This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art Grammatical Evolution (GE) methods in two directions. First, MGE's representation is modular in that each individual has a set of genes, and each gene is mapped to a neuron by grammatical rules. Second, the proposed representation mitigates two important drawbacks of GE, namely the low scalability and weak locality of representation, toward generating modular and multilayer networks with a high number of neurons. We define and evaluate five different forms of structures with and without modularity using MGE and find single-layer modules with no coupling more productive. Our experiments demonstrate that modularity helps in finding better neural networks faster. We have validated the proposed method using ten well-known classification benchmarks with different sizes, feature counts, and output class counts. Our experimental results indicate that MGE provides superior accuracy with respect to existing NeuroEvolution methods and returns classifiers that are significantly simpler than other machine learning generated classifiers. Finally, we empirically demonstrate that MGE outperforms other GE methods in terms of locality and scalability properties.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 1","pages":"291-327"},"PeriodicalIF":6.8,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46524257","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: 5

Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution 基于真实世界进化的机器人形态与控制的环境适应

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-01 DOI: 10.1162/evco_a_00291

T. F. Nygaard;C. P. Martin;D. Howard;J. Torresen;K. Glette

{"title":"Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution","authors":"T. F. Nygaard;C. P. Martin;D. Howard;J. Torresen;K. Glette","doi":"10.1162/evco_a_00291","DOIUrl":"10.1162/evco_a_00291","url":null,"abstract":"Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay among control, body, and environment are therefore rarely found. In this article, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously unseen terrains, demonstrating the generality of our approach.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 4","pages":"441-461"},"PeriodicalIF":6.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39497443","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: 15

A Self-Adaptive Response Strategy for Dynamic Multiobjective Evolutionary Optimization Based on Objective Space Decomposition 基于目标空间分解的动态多目标进化优化自适应响应策略

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-01 DOI: 10.1162/evco_a_00289

Ruochen Liu;Jianxia Li;Yaochu Jin;Licheng Jiao

{"title":"A Self-Adaptive Response Strategy for Dynamic Multiobjective Evolutionary Optimization Based on Objective Space Decomposition","authors":"Ruochen Liu;Jianxia Li;Yaochu Jin;Licheng Jiao","doi":"10.1162/evco_a_00289","DOIUrl":"10.1162/evco_a_00289","url":null,"abstract":"Dynamic multiobjective optimization deals with simultaneous optimization of multiple conflicting objectives that change over time. Several response strategies for dynamic optimization have been proposed, which do not work well for all types of environmental changes. In this article, we propose a new dynamic multiobjective evolutionary algorithm based on objective space decomposition, in which the maxi-min fitness function is adopted for selection and a self-adaptive response strategy integrating a number of different response strategies is designed to handle unknown environmental changes. The self-adaptive response strategy can adaptively select one of the strategies according to their contributions to the tracking performance in the previous environments. Experimental results indicate that the proposed algorithm is competitive and promising for solving different DMOPs in the presence of unknown environmental changes. Meanwhile, the proposed algorithm is applied to solve the parameter tuning problem of a proportional integral derivative (PID) controller of a dynamic system, obtaining better control effect.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"29 4","pages":"491-519"},"PeriodicalIF":6.8,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38847968","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: 6

Maximizing Drift Is Not Optimal for Solving OneMax 最大化漂移不是解决OneMax的最佳方案

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2021-12-01 DOI: 10.1162/evco_a_00290

Nathan Buskulic;Carola Doerr

引用次数: 21

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

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

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