2009 IEEE Congress on Evolutionary Computation最新文献_第5页

2012 IEEE Congress on Evolutionary Computation 2012年IEEE进化计算大会

2009 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256101

H. Abbass, D. Essam, R. Sarker

引用次数: 20

Evolutionary local search for solving the office space allocation problem 求解办公空间分配问题的进化局部搜索

2009 IEEE Congress on Evolutionary Computation Pub Date : 2012-01-01 DOI: 10.1109/CEC.2012.6253009

Özgür Ülker, Dario Landa Silva

引用次数: 0

Bipolar preferences dominance based evolutionary algorithm for many-objective optimization 基于双极偏好优势的多目标优化进化算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2012-01-01 DOI: 10.1109/CEC.2012.6256618

Fei-yue Qiu, Yu-shi Wu, Liping Wang, Bo Jiang

引用次数: 0

Darwin's magic: Evolutionary computation in nanoscience, bioinformatics and systems biology 达尔文的魔法:纳米科学、生物信息学和系统生物学中的进化计算

2009 IEEE Congress on Evolutionary Computation Pub Date : 2011-06-05 DOI: 10.1109/CEC.2011.5949589

N. Krasnogor

引用次数: 0

Tree-adjunct grammatical evolution 树形修饰语的语法演变

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-09-27 DOI: 10.1109/CEC.2010.5586497

E. Murphy, M. O’Neill, E. López, A. Brabazon

引用次数: 23

Dimension reduction by Manifold Learning for Evolutionary Learning with redundant sensory inputs 基于流形学习的冗余感官输入进化学习降维方法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-09-27 DOI: 10.1109/CEC.2010.5586229

H. Handa, H. Kawakami

引用次数: 2

An evolutionary approach for performing multiple sequence alignment 一种执行多序列比对的进化方法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-09-27 DOI: 10.1109/CEC.2010.5586500

F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez

{"title":"An evolutionary approach for performing multiple sequence alignment","authors":"F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez","doi":"10.1109/CEC.2010.5586500","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586500","url":null,"abstract":"Despite of being a very common task in bioinformatics, multiple sequence alignment is not a trivial matter. Arranging a set of molecular sequences to reveal their similarities and their differences is often hardened by the complexity and the size of the search space involved, which undermine the approaches that try to explore exhaustively the solution's search space. Due to its nature, Genetic Algorithms, which are prone for general combinatorial problems optimization in large and complex search spaces, emerge as serious candidates to tackle with the multiple sequence alignment problem. We have developed an evolutionary approach, AlineaGA, which uses a Genetic Algorithm with local search optimization embedded on its mutation operators for performing multiple sequence alignment. Now, we have enhanced its selection method by employing an elitist strategy, and we have also developed a new crossover operator. These transformations allow AlineaGA to improve its robustness and to obtain better fit solutions. Also, we have studied the effect of the mutation probability in solutions' evolution by analyzing the performance of the whole population throughout generations. We conclude that increasing the mutation probability leads to better solutions in fewer generations and that the mutation operators have a dramatic effect in this particular domain.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"2012 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89858919","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: 13

Statistical analysis of the parameters of the simulated annealing algorithm 统计分析了模拟退火算法的参数

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586160

M. G. Arenas, J. L. Laredo, P. Castillo, P. García-Sánchez, A. García, A. Prieto, J. J. M. Guervós

引用次数: 0

A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment 不确定对抗环境下救援路径规划的混合遗传算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586311

J. Berger, Khaled Jabeur, A. Boukhtouta, A. Guitouni, A. Ghanmi

{"title":"A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment","authors":"J. Berger, Khaled Jabeur, A. Boukhtouta, A. Guitouni, A. Ghanmi","doi":"10.1109/CEC.2010.5586311","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586311","url":null,"abstract":"Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and threat exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"6 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75853047","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: 12

Comparing lbest PSO niching algorithms using different position update rules 比较使用不同位置更新规则的最佳粒子群小生境算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586317

Xiaodong Li, K. Deb

引用次数: 25