2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第5页

Learning automata induced artificial bee colony for noisy optimization 学习自动机诱导的人工蜂群噪声优化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969415

P. Rakshit, A. Konar, A. Nagar

引用次数: 9

Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming Lexicase选择促进线性遗传规划解决方案的有效搜索和行为多样性

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969310

Karoliina Oksanen, Ting Hu

{"title":"Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming","authors":"Karoliina Oksanen, Ting Hu","doi":"10.1109/CEC.2017.7969310","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969310","url":null,"abstract":"Linear Genetic Programming (LGP) is an evolutionary algorithm aimed at solving computational problems, most common problem types being symbolic regression and classification. The standard method for selecting the parent individuals that get to undergo modification at each generation of the algorithm is tournament selection, which operates based on an aggregate fitness value computed on the whole training dataset. Lexicase selection, a novel parent selection method introduced by Lee Spector and his research group, works differently by randomly ordering the samples in the training dataset and using each of them in turn to eliminate parent candidates from consideration. As a result it allows for selecting specialist individuals, which perform well on some samples but badly on others, instead of generalist individuals whose average performance on all of the samples is good. Lexicase selection has previously been tested on tree-GP and PushGP, but not on LGP. In this study, we use three different benchmark problems to compare its performance to tournament selection, investigating the mean best fitness values of the test runs at each generation, as well as the effect of the parent selection operator on behavioural diversity. We conclude that lexicase selection drives the search towards good solutions more effectively than tournament selection, and that this effect correlates with improved behavioural diversity in most cases.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"425 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034870","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: 5

Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems 主动粒子群优化:一种无设置的实参数单目标优化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969538

A. Tangherloni, L. Rundo, Marco S. Nobile

{"title":"Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems","authors":"A. Tangherloni, L. Rundo, Marco S. Nobile","doi":"10.1109/CEC.2017.7969538","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969538","url":null,"abstract":"Particle Swarm Optimization (PSO) is an effective Swarm Intelligence technique for the optimization of non-linear and complex high-dimensional problems. Since PSO's performance is strongly dependent on the choice of its functioning settings, in this work we consider a self-tuning version of PSO, called Proactive Particles in Swarm Optimization (PPSO). PPSO leverages Fuzzy Logic to dynamically determine the best settings for the inertia weight, cognitive factor and social factor. The PPSO algorithm significantly differs from other versions of PSO relying on Fuzzy Logic, because specific settings are assigned to each particle according to its history, instead of being globally assigned to the whole swarm. In such a way, PPSO's particles gain a limited autonomous and proactive intelligence with respect to the reactive agents proposed by PSO. Our results show that PPSO achieves overall good optimization performances on the benchmark functions proposed in the CEC 2017 test suite, with the exception of those based on the Schwefel function, whose fitness landscape seems to mislead the fuzzy reasoning. Moreover, with many benchmark functions, PPSO is characterized by a higher speed of convergence than PSO in the case of high-dimensional problems.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125915018","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: 41

Efficient nonlinear correlation detection for decomposed search in evolutionary multi-objective optimization 进化多目标优化中分解搜索的高效非线性相关检测

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969372

Handing Wang, Yaochu Jin

{"title":"Efficient nonlinear correlation detection for decomposed search in evolutionary multi-objective optimization","authors":"Handing Wang, Yaochu Jin","doi":"10.1109/CEC.2017.7969372","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969372","url":null,"abstract":"The mapping relation between decision variables and objective functions is complicated in multi-objective optimization problems. Dimension reduction-based memetic optimization strategy was proposed to decompose a multi-objective optimization problem into several easier subproblems in decision subspaces by detecting the correlation between decision variables and objective functions. In this work, the process of optimizing the original problem by separately searching the decision space of the subproblems is termed decomposed search. We embed the decomposed search strategy in existing multi-objective evolutionary algorithms to improve their performance. However, it is highly time-consuming to detect the mapping relation and select solutions for decomposed search. To improve the computational efficiency of the strategy, we adopt nonlinear correlation information entropy to measure the correlation between the decision variables and objective functions and suggest a probabilistic similarity measurement to select solutions for the decomposed search, which is shown to be effective by experimental results. Finally, the correlation detection and solution selection strategies proposed in this paper are embedded in both Pareto- and non-Pareto-based multi-objective evolutionary algorithms to compare them with existing ones. Our experimental results demonstrate that the proposed strategies have significantly improved the computational efficiency at the expense of slightly degraded performance.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121462386","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: 5

A comparative study of Multiobjective Evolutionary Algorithms for Wireless Local Area Network design 无线局域网设计中的多目标进化算法比较研究

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969413

M. P. Lima, R. F. Alexandre, R. Takahashi, E. G. Carrano

引用次数: 3

Evolutionary multi-task learning for modular extremal learning machine 模块化极值学习机的进化多任务学习

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969349

Zedong Tang, Maoguo Gong, Mingyang Zhang

{"title":"Evolutionary multi-task learning for modular extremal learning machine","authors":"Zedong Tang, Maoguo Gong, Mingyang Zhang","doi":"10.1109/CEC.2017.7969349","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969349","url":null,"abstract":"Evolutionary multi-tasking is a novel concept where algorithms utilize the implicit parallelism of population-based search to solve several tasks efficiently. In last decades, multi-task learning, which harnesses the underlying similarity of the learning tasks, has proved efficient in many applications. Extreme learning machine is a distinctive learning algorithm for feed-forward neural networks. Because of its similarity and low computational complexity comparing with the convenient neural network training algorithms, it has been used in many cases of data analyses. In this paper, a modular training technique by employing evolutionary multi-task paradigm is used to evolve the modular topologies of extreme learning machine. Though, extreme learning machine is much faster than the convenient gradient-based method, it needs more hidden neurons due to the random determination of input weights. In proposed method, we combine the evolutionary extreme learning machine and multi-task modular training. Each task is defined by an evolutionary extreme learning machine with different number of hidden neurons. This method produces a modular extreme learning machine which needs less number of hidden units and could be effective even if some hidden neurons and connections are removed. Experiment results show effectiveness and generalization of the proposed method for benchmark classification problems.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563732","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: 16

A hybrid iterated greedy algorithm for the distributed no-wait flow shop scheduling problem 分布式无等待流车间调度问题的混合迭代贪心算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969289

W. Shao, D. Pi, Zhongshi Shao

引用次数: 7

Optimal placement of wind turbines in a windfarm using L-SHADE algorithm 利用L-SHADE算法优化风电场中风力涡轮机的位置

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969299

P. Biswas, P. N. Suganthan, G. Amaratunga

{"title":"Optimal placement of wind turbines in a windfarm using L-SHADE algorithm","authors":"P. Biswas, P. N. Suganthan, G. Amaratunga","doi":"10.1109/CEC.2017.7969299","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969299","url":null,"abstract":"Setting of turbines in a windfarm is a complex task as several factors need to be taken into consideration. During recent years, researchers have applied various evolutionary algorithms to windfarm layout problem by converting it to a single objective and at the most two objective optimization problem. The prime factor governing placement of turbines is the wake effect attributed to the loss of kinetic energy by wind after it passes over a turbine. Downstream turbine inside the wake region generates less output power. Optimizing the wake loss helps extract more power out of the wind. The cost of turbine is tactically entwined with generated output to form single objective of cost per unit of output power e.g. cost/kW. This paper proposes an application of L-SHADE algorithm, an advanced form of Differential Evolution (DE) algorithm, to minimize the objective cost/kW. SHADE is a success history based parameter adaptation technique of DE. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. DE has historically been used mainly for optimization of continuous variables. The present study suggests an approach of using algorithm L-SHADE in discrete location optimization problem. Case studies of varying wind directions with constant and variable wind speeds have been performed and results are compared with some of the previous studies.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124426599","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: 37

PSO-based Search mechanism in dynamic environments: Swarms in Vector Fields 动态环境中基于pso的搜索机制:向量场中的蜂群

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969450

Palina Bartashevich, Luigi Grimaldi, Sanaz Mostaghim

引用次数: 14

Ranking Multi-Objective Evolutionary Algorithms using a chess rating system with Quality Indicator ensemble 基于质量指标集成的象棋分级系统的多目标进化算法排序

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969481

Miha Ravber, M. Mernik, M. Črepinšek

引用次数: 8