2013 IEEE Congress on Evolutionary Computation最新文献_第2页

On the recombination operator in the real-coded genetic algorithms 实数编码遗传算法中的重组算子

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557948

S. Picek, D. Jakobović, M. Golub

引用次数: 35

Investigation on the performance of a new multiple choice strategy for PSO Algorithm in the task of large scale optimization problems PSO算法在大规模优化问题中一种新的选择策略的性能研究

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557805

Michal Pluhacek, R. Šenkeřík, I. Zelinka

引用次数: 8

A pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment 基于pareto的遗传算法在云代理环境下优化VM请求分配

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557869

Y. Kessaci, N. Melab, E. Talbi

引用次数: 44

Towards a multiobjective evolutionary approach to inventory and routing management in a retail chain 零售连锁企业库存与路线管理的多目标演化方法研究

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557957

Anna I. Esparcia-Alcázar, Anaís Martínez-García, P. García-Sánchez, J. J. M. Guervós, A. García

引用次数: 1

Initialization methods for large scale global optimization 大规模全局优化的初始化方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557902

B. Kazimipour, Xiaodong Li, A. K. Qin

引用次数: 59

Learning non-linear ranking functions for web search using probabilistic model building GP 利用概率模型构建GP学习网络搜索的非线性排序函数

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557983

Hiroyuki Sato, Danushka Bollegala, Yoshihiko Hasegawa, H. Iba

引用次数: 5

Multi-objective optimization of traffic externalities using tolls 基于收费的交通外部性多目标优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557865

A. Ohazulike, Ties Brands

{"title":"Multi-objective optimization of traffic externalities using tolls","authors":"A. Ohazulike, Ties Brands","doi":"10.1109/CEC.2013.6557865","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557865","url":null,"abstract":"Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multiobjective problem and compare the results with those of the wellknown genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537457","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: 7

Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization 基于局部交叉的动态优化自适应差分进化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557554

R. Mukherjee, S. Debchoudhury, Rupam Kundu, Swagatam Das, P. N. Suganthan

{"title":"Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization","authors":"R. Mukherjee, S. Debchoudhury, Rupam Kundu, Swagatam Das, P. N. Suganthan","doi":"10.1109/CEC.2013.6557554","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557554","url":null,"abstract":"Real life problems which deal with time varying landscape dynamics often pose serious challenge to the mettle of researchers in the domain of Evolutionary Computation. Classified as Dynamic Optimization problems (DOPs), these deal with candidate solutions which vary their dominance over dynamic change instances. The challenge is to efficiently recapture the dominant solution or the global optimum in each varying landscape. Differential Evolution (DE) algorithm with modifications of adaptability have been widely used to deal with the complexities of a dynamic landscape, yet problems persist unless dedicated structuring is done to exclusively deal with DOPs. In Adaptive Differential Evolution with Locality based Crossover (ADE-LbX) the mutation operation has been entrusted to a locality based scheme that retains traits of Euclidean distance based closest individuals around a potential solution. Diversity maintenance is further enhanced by incorporation of local best crossover scheme that renders the landscape independent of direction and empowers the algorithm with an explorative ability. An even distribution of solutions in different regions of landscape calls for a solution retention technique that adapts this algorithm to dynamism by using its previous information in diverse search domains. To evaluate the performance of ADE-LbX, it has been tested over Dynamic Problem instance proposed as in CEC 09 and compared with State-of-the-arts. The algorithm enjoys superior performance in varied problem configurations of the problem.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116571744","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: 11

Hybridisation of Genetic Programming and Nearest Neighbour for classification 遗传规划与最近邻分类的杂交

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557889

Harith Al-Sahaf, A. Song, Mengjie Zhang

引用次数: 8

Robot path planning in an environment with many terrains based on interval multi-objective PSO 基于区间多目标粒子群算法的多地形环境下机器人路径规划

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557652

N. Geng, D. Gong, Yong Zhang

引用次数: 14