Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation最新文献_第2页

On the efficiency of worst improvement for climbing NK-landscapes 攀爬nk景观最差改善效率研究

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598268

M. Basseur, Adrien Goëffon

引用次数: 12

An implicitly parallel EDA based on restricted boltzmann machines 基于受限玻尔兹曼机的隐式并行EDA

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598273

Malte Probst, Franz Rothlauf, Jörn Grahl

引用次数: 7

Stepsize control on the modified bacterial foraging algorithm for constrained numerical optimization 约束数值优化改进细菌觅食算法的步长控制

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598379

B. Hernández-Ocaña, Maria del Pilar Pozos Parra, E. Mezura-Montes

{"title":"Stepsize control on the modified bacterial foraging algorithm for constrained numerical optimization","authors":"B. Hernández-Ocaña, Maria del Pilar Pozos Parra, E. Mezura-Montes","doi":"10.1145/2576768.2598379","DOIUrl":"https://doi.org/10.1145/2576768.2598379","url":null,"abstract":"The stepsize value is one of the most sensitive parameters in the bacterial foraging optimization algorithm when solving constrained numerical optimization problems. In this paper, four stepsize control mechanisms are proposed and analyzed in the modified bacterial foraging optimization algorithm. The first one is based on a random value which remains fixed during the search, the second one generates a random value per cycle, the third one is based on a nonlinear decreasing function and the last one is an adaptive approach. Seven experiments are proposed to evaluate the abilities of each mechanism to: (1) obtain competitive final results, (2) find feasible solutions, (3) find the feasible global optimum, (4) promote successful swims, and (5) decrease the constraint violation. A comparison against two state-of-the-art algorithms is considered to evaluate the performance of the most competitive control mechanism. A well-known set of constrained numerical optimization problems is used in the experiments as well as six performance measures. The results obtained show that the control mechanism based on the nonlinear decreasing function is the most competitive and provides the ability to generate better solutions late in the search.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116931343","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

Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization MOEA/D中的倒PBI及其对多目标优化搜索性能的影响

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598297

Hiroyuki Sato

{"title":"Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization","authors":"Hiroyuki Sato","doi":"10.1145/2576768.2598297","DOIUrl":"https://doi.org/10.1145/2576768.2598297","url":null,"abstract":"MOEA/D decomposes a multi-objective optimization problem into a number of single objective optimization problems. Each single objective optimization problem is defined by a scalarizing function using a weight vector. In MOEA/D, there are several scalarizing approaches such as the weighted Tchebycheff, the weighted sum, and the PBI (penalty-based boundary intersection). However, these conventional scalarizing approaches face a difficulty to approximate a widely spread Pareto front in some problems. To enhance the spread of Pareto optimal solutions in the objective space and improve the search performance of MOEA/D especially in many-objective optimization problems, in this work we propose the inverted PBI scalarizing approach which is an extension of the conventional PBI. We use many-objective knapsack problems and WFG4 problems with 2-8 objectives, and compare the search performance of NSGA-III and four MOEA/Ds using the weighted Tchebycheff, the weighted sum, the PBI and the inverted PBI. As results, we show that MOEA/D using the inverted PBI achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to obtain a widely spread Pareto front in the objective space.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192441","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: 107

On homogenization of coal in longitudinal blending beds 纵向配煤床中煤的均质性研究

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598316

P. Shukla, Michael P. Cipold, C. Bachmann, H. Schmeck

{"title":"On homogenization of coal in longitudinal blending beds","authors":"P. Shukla, Michael P. Cipold, C. Bachmann, H. Schmeck","doi":"10.1145/2576768.2598316","DOIUrl":"https://doi.org/10.1145/2576768.2598316","url":null,"abstract":"Coal blending processes mainly use static and non-reactive blending methods like the well-known Chevron stacking. Although real-time quality measurement techniques such as online X-ray fluorescence measurements are available, the possibility to explore a dynamic adaptation of the blending process to the current quality data obtained using these techniques has not been explored. A dynamic adaptation helps to mix the coal from different mines in an optimal way and deliver a homogeneous product. The paper formulates homogenization of coal in longitudinal blending beds as a bi-objective problem of minimizing the variance of the cross-sectional quality and minimizing the height variance of the coal heap in the blending bed. We propose a cone based evolutionary algorithm to explore different trade-off regions of the Pareto front. A pronounced knee region on the Pareto front is found and is investigated in detail using a knee search algorithm. There are many interesting problem insights that are gained by examining the solutions found in different regions. In addition, all the knee solutions outperform the traditional Chevron stacking method.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604954","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

Novelty search creates robots with general skills for exploration 新奇搜索创造了具有一般探索技能的机器人

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598225

R. Velez, J. Clune

{"title":"Novelty search creates robots with general skills for exploration","authors":"R. Velez, J. Clune","doi":"10.1145/2576768.2598225","DOIUrl":"https://doi.org/10.1145/2576768.2598225","url":null,"abstract":"Novelty Search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. Instead of selecting for phenotypes that are closer to an objective, Novelty Search assigns rewards based on how different the phenotypes are from those already generated. A common criticism of Novelty Search is that it is effectively random or exhaustive search because it tries solutions in an unordered manner until a correct one is found. Its creators respond that over time Novelty Search accumulates information about the environment in the form of skills relevant to reaching uncharted territory, but to date no evidence for that hypothesis has been presented. In this paper we test that hypothesis by transferring robots evolved under Novelty Search to new environments (here, mazes) to see if the skills they've acquired generalize. Three lines of evidence support the claim that Novelty Search agents do indeed learn general exploration skills. First, robot controllers evolved via Novelty Search in one maze and then transferred to a new maze explore significantly more of the new environment than non-evolved (randomly generated) agents. Second, a Novelty Search process to solve the new mazes works significantly faster when seeded with the transferred controllers versus randomly-generated ones. Third, no significant difference exists when comparing two types of transferred agents: those evolved in the original maze under (1) Novelty Search vs. (2) a traditional, objective-based fitness function. The evidence gathered suggests that, like traditional Evolutionary Algorithms with objective-based fitness functions, Novelty Search is not a random or exhaustive search process, but instead is accumulating information about the environment, resulting in phenotypes possessing skills needed to explore their world.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129802808","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: 32

Genetic algorithms and deep learning for automatic painter classification 基于遗传算法和深度学习的自动画家分类

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598287

Erez Levy, Omid David, N. Netanyahu

引用次数: 31

Performance of metropolis algorithm for the minimum weight code word problem 最小权码字问题的metropolis算法性能

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598274

Ajitha Shenoy K B, Somenath Biswas, Piyush P. Kurur

引用次数: 4

EVOR: an online evolutionary algorithm for car racing games EVOR:赛车游戏的在线进化算法

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598298

Samadhi Nallaperuma, F. Neumann, M. Bonyadi, Z. Michalewicz

引用次数: 6

Learning the structure of large-scale bayesian networks using genetic algorithm 使用遗传算法学习大规模贝叶斯网络的结构

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598223

F. Vafaee

引用次数: 17