2013 IEEE Congress on Evolutionary Computation最新文献_第4页

Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization 2013 CEC专题会议的可变网格优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557557

D. Molina, Amilkar Puris, Rafael Bello, F. Herrera

{"title":"Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization","authors":"D. Molina, Amilkar Puris, Rafael Bello, F. Herrera","doi":"10.1109/CEC.2013.6557557","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557557","url":null,"abstract":"Many real-world problems have several optima, and the aim of niching optimisation algorithms is to obtain the different global optima, and not only the best solution. One common technique to create niches is the clearing method that removes solutions too close to better ones. Unfortunately, clearing is very sensitive to the niche radius, and its right value depends on the problem (in real-world problems the minimum distance between optima is unknown). In this work we propose a niching algorithm that uses clearing with an adaptive niche radius, that decreases during the run. The proposal uses an external memory that stores current global optima to avoid losing found optima during the clearing process, allowing a non-elitist search. This algorithm applies this clearing method to a mesh of solutions, expanded by the generation of nodes using combination methods between the nodes, their best neighbour, and their nearest current global optima in the population (current global optima are nodes with fitness very similar to current best fitness). The proposal is tested on the competition benchmark proposed in the Special Session Niching Methods for Multimodal Optimization, and compared with other algorithms. The proposal obtains very good results detecting global optima. In comparisons with other algorithm, this proposal obtains the best results, proving to be a very competitive niching algorithm.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123895546","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: 29

Learning the Caesar and Vigenere Cipher by hierarchical evolutionary re-combination 通过等级进化重组学习凯撒和维吉内尔密码

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557624

A. Blair

引用次数: 14

A ranking method based on the R2 indicator for many-objective optimization 基于R2指标的多目标优化排序方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557743

Alan Díaz-Manríquez, G. T. Pulido, C. Coello, R. Becerra

{"title":"A ranking method based on the R2 indicator for many-objective optimization","authors":"Alan Díaz-Manríquez, G. T. Pulido, C. Coello, R. Becerra","doi":"10.1109/CEC.2013.6557743","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557743","url":null,"abstract":"In recent years, the development of selection mechanisms based on performance indicators has become an important trend in algorithmic design. Hereof, the hypervolume has been the most popular choice. Multi-objective evolutionary algorithms (MOEAs) based on this indicator seem to be a good choice for dealing with many-objective optimization problems. However, their main drawback is that such algorithms are typically computationally expensive. This has motivated some recent research in which the use of other performance indicators has been explored. Here, we propose an efficient mechanism to integrate the R2 indicator to a modified version of Goldberg's nondominated sorting method, in order to rank the individuals of a MOEA. Our proposed ranking scheme is coupled to two different search engines, resulting in two new MOEAs. These MOEAs are validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed ranking approach gives rise to effective MOEAs, which produce results that are competitive with respect to those obtained by three well-known MOEAs. Additionally, we validate our resulting MOEAs in many-objective optimization problems, in which our proposed ranking scheme shows its main advantage, since it is able to outperform a hypervolume-based MOEA, requiring a much lower computational time.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123686393","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: 43

Point representation for local optimization 局部优化的点表示

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557661

S. Baluja, Michele Covell

引用次数: 0

Studying feedback mechanisms for adaptive parameter control in evolutionary algorithms 研究进化算法中自适应参数控制的反馈机制

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557950

A. Aleti, I. Moser

{"title":"Studying feedback mechanisms for adaptive parameter control in evolutionary algorithms","authors":"A. Aleti, I. Moser","doi":"10.1109/CEC.2013.6557950","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557950","url":null,"abstract":"The performance of an Evolutionary Algorithm (EA) is greatly affected by the settings of its strategy parameters. An effective solution to the parameterisation problem is adaptive parameter control, which applies learning methods that use feedback from the optimisation process to evaluate the effect of parameter value choices and adjust the parameter values over the iterations. At every iteration of an EA, the performance of an EA is reported and employed by the feedback mechanism as an indication of the success of the parameterisation of the algorithm instance. Many approaches to collect information about the algorithm's performance exist in single objective optimisation. In this work, we review the most recent and prominent approaches. In multiobjective optimisation, establishing a single scalar which can report the algorithm's performance as feedback for adaptive parameter control is a complex task. Existing performance measures of multiobjective optimisation are generally used as feedback for the optimisation process. We discuss the properties of these measures and present an empirical evaluation of the binary hypervolume and ϵ+-indicators as feedback for adaptive parameter control.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335356","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

Evolutionary cellular automata bonsai 进化细胞自动机盆景

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557587

D. Ashlock, Carolyn Pugh

{"title":"Evolutionary cellular automata bonsai","authors":"D. Ashlock, Carolyn Pugh","doi":"10.1109/CEC.2013.6557587","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557587","url":null,"abstract":"Cellular automata are known to be capable of Turing-complete computation and yet “programming” them to do particular tasks can be quite daunting. In this paper we use single parent crossover as a means of transferring information between successive evolving populations to create rules for cellular automata that have proscribed shapes. The proscription of regions where the automata are permitted to grow is the reason they are called bonsai automata. This work follows earlier work on apoptotic cellular automata that simply exhibit self-limited growth. The correct choice of single parents permits enormous improvement in the performance of evolutionary algorithms searching for automata that satisfy particular bonsai templates. In this study, we demonstrate that single parent techniques make meeting shape constraints on the growth of CAs possible at all in some cases. This study also introduces range niche specialization to control problems with the cloning of ancestors used for single parent crossover in an evolving population. This study demonstrates that different bonsai shapes have highly variable difficulty. It is also shown that automata evolved to satisfy one bonsai template may be needed to enable, via single parent crossover, solutions for another template. The use of bonsai techniques yields many automata not found during studies of apoptotic automata demonstrating that the technique encourages exploration of different parts of the fitness landscape.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756149","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: 2

Fixed-parameter evolutionary algorithms for the Euclidean Traveling Salesperson problem 欧氏旅行推销员问题的固定参数进化算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557809

Samadhi Nallaperuma, Andrew M. Sutton, F. Neumann

{"title":"Fixed-parameter evolutionary algorithms for the Euclidean Traveling Salesperson problem","authors":"Samadhi Nallaperuma, Andrew M. Sutton, F. Neumann","doi":"10.1109/CEC.2013.6557809","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557809","url":null,"abstract":"Recently, Sutton and Neumann [1] have studied evolutionary algorithms for the Euclidean traveling salesman problem by parameterized runtime analyses taking into account the number of inner points k and the number of cities n. They have shown that simple evolutionary algorithms are XP-algorithms for the problem, i.e., they obtain an optimal solution in expected time O(ng(k)) where g(k) is a function only depending on k. We extend these investigations and design two evolutionary algorithms for the Euclidean Traveling Salesperson problem that run in expected time g(k) · poly(n) where k is a parameter denoting the number inner points for the given TSP instance, i.e., they are fixed-parameter tractable evolutionary algorithms for the Euclidean TSP parameterized by the number of inner points. While our first approach is mainly of theoretical interest, our second approach leverages problem structure by directly searching for good orderings of the inner points and provides a novel and highly effective way of tackling this important problem. Our experimental results show that searching for a permutation on the inner points is a significantly powerful practical strategy.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787174","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

On evolving neighborhood parameters for fuzzy density clustering 模糊密度聚类中邻域参数的演化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557970

A. Banerjee

引用次数: 1

PSO hybrid intelligent inverse optimal control for an anaerobic process 厌氧过程的粒子群混合智能逆最优控制

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557660

K. J. Gurubel, E. Sánchez, S. Carlos-Hernandez, Fernando Ornelas

引用次数: 3

Shifting niches for community structure detection 移动生态位用于社区结构检测

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557560

Corrado Grappiolo, J. Togelius, Georgios N. Yannakakis

引用次数: 4