Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation最新文献

Online model racing based on extreme performance 基于极限性能的在线模型赛车

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598336

Tiantian Zhang, M. Georgiopoulos, G. Anagnostopoulos

引用次数: 7

Use of explicit memory in the dynamic traveling salesman problem 外显记忆在动态旅行商问题中的应用

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598247

R. Tinós, L. D. Whitley, A. Howe

引用次数: 19

On the performance of multiple objective evolutionary algorithms for software architecture discovery 多目标进化算法在软件架构发现中的性能研究

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598310

Aurora Ramírez, J. Romero, Sebastián Ventura

{"title":"On the performance of multiple objective evolutionary algorithms for software architecture discovery","authors":"Aurora Ramírez, J. Romero, Sebastián Ventura","doi":"10.1145/2576768.2598310","DOIUrl":"https://doi.org/10.1145/2576768.2598310","url":null,"abstract":"During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure these systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Its abstract and highly combinatorial nature increases the complexity of the problem. In this scenario, Search-based Software Engineering (SBSE) may serve to support this decision making process from initial analysis models, since the discovery of component-based architectures can be formulated as a challenging multiple optimisation problem, where different metrics and configurations can be applied depending on the design requirements and its specific domain. Many-objective optimisation evolutionary algorithms can provide an interesting alternative to classical multi-objective approaches. This paper presents a comparative study of five different algorithms, including an empirical analysis of their behaviour in terms of quality and variety of the returned solutions. Results are also discussed considering those aspects of concern to the expert in the decision making process, like the number and type of architectures found. The analysis of many-objectives algorithms constitutes an important challenge, since some of them have never been explored before in SBSE.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"21 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":"124396951","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

Efficient global optimization for combinatorial problems 组合问题的高效全局优化

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598282

Martin Zaefferer, Jörg Stork, Martina Friese, A. Fischbach, B. Naujoks, T. Bartz-Beielstein

{"title":"Efficient global optimization for combinatorial problems","authors":"Martin Zaefferer, Jörg Stork, Martina Friese, A. Fischbach, B. Naujoks, T. Bartz-Beielstein","doi":"10.1145/2576768.2598282","DOIUrl":"https://doi.org/10.1145/2576768.2598282","url":null,"abstract":"Real-world optimization problems may require time consuming and expensive measurements or simulations. Recently, the application of surrogate model-based approaches was extended from continuous to combinatorial spaces. This extension is based on the utilization of suitable distance measures such as Hamming or Swap Distance. In this work, such an extension is implemented for Kriging (Gaussian Process) models. Kriging provides a measure of uncertainty when determining predictions. This can be harnessed to calculate the Expected Improvement (EI) of a candidate solution. In continuous optimization, EI is used in the Efficient Global Optimization (EGO) approach to balance exploitation and exploration for expensive optimization problems. Employing the extended Kriging model, we show for the first time that EGO can successfully be applied to combinatorial optimization problems. We describe necessary adaptations and arising issues as well as experimental results on several test problems. All surrogate models are optimized with a Genetic Algorithm (GA). To yield a comprehensive comparison, EGO and Kriging are compared to an earlier suggested Radial Basis Function Network, a linear modeling approach, as well as model-free optimization with random search and GA. EGO clearly outperforms the competing approaches on most of the tested problem instances.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"112 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":"117241719","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: 52

Robust next release problem: handling uncertainty during optimization 健壮的下一个发布问题:处理优化过程中的不确定性

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598334

Lingbo Li, M. Harman, Emmanuel Letier, Yuanyuan Zhang

引用次数: 40

Genetic algorithm-based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation 基于遗传算法的未知尺寸和方向的超大型多重拼图求解器

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598289

Dror Sholomon, Omid David, N. Netanyahu

引用次数: 8

Identifying and exploiting the scale of a search space in particle swarm optimization 粒子群优化中搜索空间尺度的识别与开发

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598280

Yasser González-Fernández, Stephen Y. Chen

{"title":"Identifying and exploiting the scale of a search space in particle swarm optimization","authors":"Yasser González-Fernández, Stephen Y. Chen","doi":"10.1145/2576768.2598280","DOIUrl":"https://doi.org/10.1145/2576768.2598280","url":null,"abstract":"Multi-modal optimization involves two distinct tasks: identifying promising attraction basins and finding the local optima in these basins. Unfortunately, the second task can interfere with the first task if they are performed simultaneously. Specifically, the promise of an attraction basin is often estimated by the fitness of a single sample solution, so an attraction basin represented by a random sample solution can appear to be less promising than an attraction basin represented by its local optimum. The goal of thresheld convergence is to prevent these biased comparisons by disallowing local search while global search is still in progress. Ideally, thresheld convergence achieves this goal by using a distance threshold that is correlated to the size of the attraction basins in the search space. In this paper, a clustering-based method is developed to identify the scale of the search space which thresheld convergence can then exploit. The proposed method employed in the context of a multi-start particle swarm optimization algorithm has led to large improvements across a broad range of multi-modal problems.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"328 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":"124298806","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: 9

Wolfpack-inspired evolutionary algorithm and a reaction-diffusion-based controller are used for pattern formation 采用狼群进化算法和基于反应扩散的控制器进行模式形成

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598262

Payam Zahadat, T. Schmickl

引用次数: 7

Generational neuro-evolution: restart and retry for improvement 代际神经进化:重新启动和重试改进

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598295

D. Shorten, G. Nitschke

引用次数: 1

Enhancing genetic algorithm-based genome-scale metabolic network curation efficiency 提高基于遗传算法的基因组级代谢网络管理效率

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI: 10.1145/2576768.2598218

Eddy J. Bautista, R. Srivastava

{"title":"Enhancing genetic algorithm-based genome-scale metabolic network curation efficiency","authors":"Eddy J. Bautista, R. Srivastava","doi":"10.1145/2576768.2598218","DOIUrl":"https://doi.org/10.1145/2576768.2598218","url":null,"abstract":"Genome-scale metabolic modeling using constraint-based analysis is a powerful modeling paradigm for simulating metabolic networks. Models are generated via inference from genome annotations. However, errors in the annotation or the identity of a gene's function could lead to \"metabolic inconsistency\" rendering simulations infeasible. Uncovering the source of metabolic inconsistency is non-trivial due to network size and complexity. Recently published work uses genetic algorithms for curation by generating pools of models with randomly relaxed mass balance constraints. Models are evolved that allow feasible simulation while minimizing the number of constraints relaxed. Relaxed constraints represent metabolites likely to be the root of metabolic inconsistency. Although effective, the approach can result in numerous false positives. Here we present a strategy, MassChecker, which evaluates all of the relaxed mass balance constraints in each generation prior to the next round of evolution to determine if they had become consistent due to recombination/mutation. If so, these constraints are enforced. This approach was applied to the development of genome-scale metabolic model of B. anthracis. The model consisted of 1,049 reactions and 1,003 metabolites. The result was a 60% reduction in the number of relaxed mass balance constraints, significantly speeding up the curation process.","PeriodicalId":123241,"journal":{"name":"Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"15 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":"130033642","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: 0