Evolutionary Computation最新文献_第7页

Selection Heuristics on Semantic Genetic Programming for Classification Problems 分类问题的语义遗传规划选择启发式算法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00297

Claudia N. Sánchez;Mario Graff

{"title":"Selection Heuristics on Semantic Genetic Programming for Classification Problems","authors":"Claudia N. Sánchez;Mario Graff","doi":"10.1162/evco_a_00297","DOIUrl":"10.1162/evco_a_00297","url":null,"abstract":"Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals' semantics for choosing parents that enhance the addition, Naive Bayes, and Nearest Centroid. To the best of our knowledge, this is the first time that functions' properties are used for guiding the learning process. As the heuristics were created based on the properties of these functions, we apply them only when they are used to create offspring. The similarity functions considered are the cosine similarity, Pearson's correlation, and agreement. We analyze these heuristics' performance against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of parent selection based on agreement and random selection to replace an individual in the population produces statistically better results than the classical selection and state-of-the-art schemes, and it is competitive with state-of-the-art classifiers. Finally, the code is released as open-source software.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"253-289"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39555351","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

Modular Grammatical Evolution for the Generation of Artificial Neural Networks 用于生成人工神经网络的模块语法进化

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00302

Khabat Soltanian;Ali Ebnenasir;Mohsen Afsharchi

{"title":"Modular Grammatical Evolution for the Generation of Artificial Neural Networks","authors":"Khabat Soltanian;Ali Ebnenasir;Mohsen Afsharchi","doi":"10.1162/evco_a_00302","DOIUrl":"10.1162/evco_a_00302","url":null,"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 2","pages":"291-327"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39701853","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

Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times 基于迁移学习的协同代理进化双目标优化方法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00300

Xilu Wang;Yaochu Jin;Sebastian Schmitt;Markus Olhofer

引用次数: 11

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm 基于组合多目标进化算法的多踏脚石进化多模式机器人行为

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00301

Joost Huizinga;Jeff Clune

{"title":"Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm","authors":"Joost Huizinga;Jeff Clune","doi":"10.1162/evco_a_00301","DOIUrl":"10.1162/evco_a_00301","url":null,"abstract":"Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multiobjective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and ε-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"131-164"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39744129","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

VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management VSD-MOEA：一种具有显式可变空间分集管理的基于优势的多目标进化算法

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-06-01 DOI: 10.1162/evco_a_00299

Joel Chacón Castillo;Carlos Segura;Carlos A. Coello Coello

{"title":"VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management","authors":"Joel Chacón Castillo;Carlos Segura;Carlos A. Coello Coello","doi":"10.1162/evco_a_00299","DOIUrl":"10.1162/evco_a_00299","url":null,"abstract":"Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective space. Our novel Variable Space Diversity-based MOEA (vsd-moea) explicitly considers the diversity of both decision variable and objective function space. This information is used with the aim of properly adapting the balance between exploration and intensification during the optimization process. Particularly, at the initial stages, decisions made by the approach are more biased by the information on the diversity of the variable space, whereas it gradually grants more importance to the diversity of objective function space as the evolution progresses. The latter is achieved through a novel density estimator. The new method is compared with state-of-art moeas using several benchmarks with two and three objectives. This novel proposal yields much better results than state-of-the-art schemes when considering metrics applied on objective function space, exhibiting a more stable and robust behavior.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 2","pages":"195-219"},"PeriodicalIF":6.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39698125","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

Convergence Analysis of the Hessian Estimation Evolution Strategy Hessian估计进化策略的收敛性分析

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-03-01 DOI: 10.1162/evco_a_00295

Tobias Glasmachers;Oswin Krause

引用次数: 1

Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation 双模优化中限制性锦标赛选择的运行时间分析

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-03-01 DOI: 10.1162/evco_a_00292

Edgar Covantes Osuna;Dirk Sudholt

{"title":"Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation","authors":"Edgar Covantes Osuna;Dirk Sudholt","doi":"10.1162/evco_a_00292","DOIUrl":"10.1162/evco_a_00292","url":null,"abstract":"Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel, and to reduce the effect of genetic drift. We present the first rigorous runtime analyses of restricted tournament selection (RTS), embedded in a (μ+1) EA, and analyse its effectiveness at finding both optima of the bimodal function TwoMax. In RTS, an offspring competes against the closest individual, with respect to some distance measure, amongst w (window size) population members (chosen uniformly at random with replacement), to encourage competition within the same niche. We prove that RTS finds both optima on TwoMax efficiently if the window size w is large enough. However, if w is too small, RTS fails to find both optima even in exponential time, with high probability. We further consider a variant of RTS selecting individuals for the tournament without replacement. It yields a more diverse tournament and is more effective at preventing one niche from taking over the other. However, this comes at the expense of a slower progress towards optima when a niche collapses to a single individual. Our theoretical results are accompanied by experimental studies that shed light on parameters not covered by the theoretical results and support a conjectured lower runtime bound.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 1","pages":"1-26"},"PeriodicalIF":6.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39499752","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

An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming 线性遗传规划中无效指令的影响分析

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-03-01 DOI: 10.1162/evco_a_00296

Léo Françoso Dal Piccol Sotto;Franz Rothlauf;Vinícius Veloso de Melo;Márcio P. Basgalupp

{"title":"An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming","authors":"Léo Françoso Dal Piccol Sotto;Franz Rothlauf;Vinícius Veloso de Melo;Márcio P. Basgalupp","doi":"10.1162/evco_a_00296","DOIUrl":"10.1162/evco_a_00296","url":null,"abstract":"Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian Genetic Programming (CGP). This article studies four hypotheses on the role of structural introns: noneffective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3) allow neutral search, where structural introns increase the number of neutral mutations and improve performance, and (4) serve as genetic material to enable program growth. We study different variants of LGP controlling the influence of introns for symbolic regression, classification, and digital circuits problems. We find that there is (1) evolved information in the noneffective instructions that can be reactivated and that (2) structural introns can promote programs with higher effective diversity. However, both effects have no influence on LGP search performance. On the other hand, allowing mutations to not only be applied to effective but also to noneffective instructions (3) increases the rate of neutral mutations and (4) contributes to program growth by making use of the genetic material available as structural introns. This comes along with a significant increase of LGP performance, which makes structural introns important for LGP.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 1","pages":"51-74"},"PeriodicalIF":6.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39339617","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

High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection 基于遗传规划的高维非平衡二值分类多准则适合度评价与选择

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-03-01 DOI: 10.1162/evco_a_00304

Wenbin Pei;Bing Xue;Lin Shang;Mengjie Zhang

{"title":"High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection","authors":"Wenbin Pei;Bing Xue;Lin Shang;Mengjie Zhang","doi":"10.1162/evco_a_00304","DOIUrl":"10.1162/evco_a_00304","url":null,"abstract":"High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its built-in capability to select informative features. However, once data are not evenly distributed, GP tends to develop biased classifiers which achieve a high accuracy on the majority class but a low accuracy on the minority class. Unfortunately, the minority class is often at least as important as the majority class. It is of importance to investigate how GP can be effectively utilized for high-dimensional unbalanced classification. In this article, to address the performance bias issue of GP, a new two-criterion fitness function is developed, which considers two criteria, that is, the approximation of area under the curve (AUC) and the classification clarity (i.e., how well a program can separate two classes). The obtained values on the two criteria are combined in pairs, instead of summing them together. Furthermore, this article designs a three-criterion tournament selection to effectively identify and select good programs to be used by genetic operators for generating offspring during the evolutionary learning process. The experimental results show that the proposed method achieves better classification performance than other compared methods.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 1","pages":"99-129"},"PeriodicalIF":6.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39583649","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: 4

Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge 形状约束符号回归——利用先验知识改进外推

IF 6.8 2区计算机科学

Evolutionary Computation Pub Date : 2022-03-01 DOI: 10.1162/evco_a_00294

G. Kronberger;F. O. de Franca;B. Burlacu;C. Haider;M. Kommenda

{"title":"Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge","authors":"G. Kronberger;F. O. de Franca;B. Burlacu;C. Haider;M. Kommenda","doi":"10.1162/evco_a_00294","DOIUrl":"10.1162/evco_a_00294","url":null,"abstract":"We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for example, monotonicity of the function over selected inputs. The aim is to find models which conform to expected behavior and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shape-constrained symbolic regression: (i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and (ii) a two-population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models.","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":"30 1","pages":"75-98"},"PeriodicalIF":6.8,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39499749","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