2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第8页

An improved fireworks algorithm with landscape information for balancing exploration and exploitation 一种基于景观信息的烟花平衡勘探开发改进算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257035

Junfeng Chen, Qiwen Yang, J. Ni, Yingjuan Xie, Shi Cheng

引用次数: 9

Saturation in PSO neural network training: Good or evil? PSO神经网络训练中的饱和:好还是坏?

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256883

Anna Sergeevna Bosman, A. Engelbrecht

{"title":"Saturation in PSO neural network training: Good or evil?","authors":"Anna Sergeevna Bosman, A. Engelbrecht","doi":"10.1109/CEC.2015.7256883","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256883","url":null,"abstract":"Particle swarm optimisation has been successfully applied as a neural network training algorithm before, often outperforming traditional gradient-based approaches. However, recent studies have shown that particle swarm optimisation does not scale very well, and performs poorly on high-dimensional neural network architectures. This paper hypothesises that hidden layer saturation is a significant factor contributing to the poor training performance of the particle swarms, hindering good performance on neural networks regardless of the architecture size. A selection of classification problems is used to test this hypothesis. It is discovered that although a certain degree of saturation is necessary for successful training, higher degrees of saturation ultimately lead to poor generalisation. Possible factors leading to saturation are suggested, and means of alleviating saturation in particle swarms through weight initialisation range, maximum velocity, and search space boundaries are analysed. This paper is intended as a preface to a more in-depth study of the problem of saturation in particle swarm optimisation as a neural network training algorithm.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

A configurable generalized artificial bee colony algorithm with local search strategies 一种具有局部搜索策略的可配置广义人工蜂群算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257008

D. Aydın, T. Stützle

{"title":"A configurable generalized artificial bee colony algorithm with local search strategies","authors":"D. Aydın, T. Stützle","doi":"10.1109/CEC.2015.7257008","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257008","url":null,"abstract":"In this paper, we apply a generalized artificial bee colony (ABC-X) algorithm to the learning-based real-parameter optimization competition at the 2015 Congress on Evolutionary Computation. The main idea underlying the ABC-X algorithm is to provide a flexible, freely configurable framework for artificial bee colony (ABC) algorithms. From this framework, one can not only instantiate known ABC algorithms but also configure new, previously unseen ABC algorithms that may perform even better than known ABC algorithms. One key advantage of a configurable algorithm framework is that it is adaptable to many different specific problems without requiring necessarily an algorithm re-design. This is relevant if in the application problem repeatedly instances of the problem need to be solved regularly. This situation arises in many practical settings e.g. in power control or other application areas: Routinely a sequence of specific instances of a more general continuous optimization problem arise and these instances have to be solved repeatedly (possibly for an infinite horizon) in the future: in this case the instances of the problem in the sequence will share similarities as they arise from a same source. This is also the situation that is targeted by the learning-based real-parameter optimization competition and which we have also described in our own earlier research.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122223621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Feature subset selection using dynamic mixed strategy 采用动态混合策略选择特征子集

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256955

Hongbin Dong, Xuyang Teng, Yang Zhou, Jun He

{"title":"Feature subset selection using dynamic mixed strategy","authors":"Hongbin Dong, Xuyang Teng, Yang Zhou, Jun He","doi":"10.1109/CEC.2015.7256955","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256955","url":null,"abstract":"Feature selection is an important part of machine learning and data mining which may enhance the speed and the performance of learning and mining algorithms. Given certain criteria to evaluate features, the problem of feature selection can be regarded as an optimization problem. Therefore, evolutionary algorithms can be used to solve such a kind of optimization problems. In this paper, we present a novel feature subset selection approach based on the framework of genetic algorithms. Two new mutation operators are constructed using the standard deviation of candidate features and the cardinality of candidate feature subsets. Then, a filter feature subset selection approach using a dynamic mixed strategy is proposed, which combines the new mutation operators with the single-point mutation operator. The new approach can not only dynamically adjust the probability distribution over these three mutation operators, but also maintain the combined effects of feature subsets as a whole fitness evaluation. The proposed approach is able to quickly escape from local optimal feature subsets and to obtain smaller scale subsets than evolutionary algorithms using a single mutation operator. Experiments have been implemented on six standard UCI datasets and the proposed algorithm is compared with other classical algorithms. The comparison outcomes confirm the effectiveness of our approach.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"24 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120890352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Robust stock value prediction using support vector machines with particle swarm optimization 基于粒子群优化的支持向量机鲁棒股票价值预测

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257306

Trevor M. Sands, D. Tayal, M. E. Morris, S. Monteiro

引用次数: 27

Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-based machine learning 多模式启发式规则生成在多目标模糊遗传机器学习中的作用

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257262

Y. Nojima, Kazuhiro Watanabe, H. Ishibuchi

{"title":"Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-based machine learning","authors":"Y. Nojima, Kazuhiro Watanabe, H. Ishibuchi","doi":"10.1109/CEC.2015.7257262","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257262","url":null,"abstract":"Fuzzy genetics-based machine learning (FGBML) has frequently been used for fuzzy classifier design. It is one of the promising evolutionary machine learning (EML) techniques from the viewpoint of data mining. This is because FGBML can generate accurate classifiers with linguistically interpretable fuzzy if-then rules. Of course, a classifier with tens of thousands of if-then rules is not linguistically understandable. Thus, the complexity minimization of fuzzy classifiers should be considered together with the accuracy maximization. In previous studies, we proposed hybrid FGBML and its multiobjective formulation (MoFGBML) to handle both the accuracy maximization and the complexity minimization simultaneously. MoFGBML can obtain a number of non-dominated classifiers with different tradeoffs between accuracy and complexity. In this paper, we focus on heuristic rule generation in MoFGBML to improve the search performance. In the original heuristic rule generation, each if-then rule is generated from a randomly-selected training pattern in a heuristic manner. This operation is performed at population initialization and during evolution. To generate more generalized rules according to the training data, we propose new heuristic rule generation where each rule is generated from multiple training patterns. Through computational experiments using some benchmark data sets, we discuss the effects of the proposed operation on the search performance of our MoFGBML.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123759190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Evolving spiking neural network for robot locomotion generation 基于脉冲神经网络的机器人运动生成

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256939

Noriko Takase, János Botzheim, N. Kubota

引用次数: 6

Fuzzy neural tree in evolutionary computation for architectural design cognition 建筑设计认知进化计算中的模糊神经树

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257171

Ö. Ciftcioglu, M. Bittermann

引用次数: 1

A new non-redundant objective set generation algorithm in many-objective optimization problems 多目标优化问题中一种新的无冗余目标集生成算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257243

Xiaofang Guo, Yuping Wang, Xiaoli Wang, Jingxuan Wei

引用次数: 2

A self-adaptive dynamic particle swarm optimizer 一种自适应动态粒子群优化算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257290

Jing J. Liang, L. Guo, R. Liu, B. Qu

引用次数: 23