2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第6页

Exploring the landscape of the space of heuristics for local search in SAT 探索SAT中启发式局部搜索空间的格局

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969611

Andrew W. Burnett, A. Parkes

{"title":"Exploring the landscape of the space of heuristics for local search in SAT","authors":"Andrew W. Burnett, A. Parkes","doi":"10.1109/CEC.2017.7969611","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969611","url":null,"abstract":"Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, and so finding good ones is in itself a challenging search problem. A natural method to search for effective heuristics is to represent the heuristic as a small program and then apply evolutionary methods, such as genetic programming. However, the search within the space of heuristics is not well understood, and in particular little is known of the associated search landscapes. In this paper, we consider the domain of propositional satisfiability (SAT), and a generic class of local search methods called ‘WalkSAT’. We give a language for generating the heuristics; using this we generated over three million heuristics, in a systematic manner, and evaluated their associated fitness values. We then use this data set as the basis for an initial analysis of the landscape of the space of heuristics. We give evidence that the heuristic landscape exhibits clustering. We also consider local search on the space of heuristics and show that it can perform quite well, and could complement genetic programming methods on that space.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122687995","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: 3

Investigating IKK dynamics in the NF-κB signalling pathway using X-Machines 利用X-Machines研究NF-κB信号通路中的IKK动态

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969320

R. Williams, J. Timmis, E. Qwarnstrom

引用次数: 3

Adaptive constraint handling and Success History Differential Evolution for CEC 2017 Constrained Real-Parameter Optimization CEC 2017约束实参数优化的自适应约束处理与成功历史差分演化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969601

A. Zamuda

引用次数: 43

A modified indicator-based evolutionary algorithm (mIBEA) 一种改进的基于指标的进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969423

Wenwen Li, E. Özcan, R. John, J. Drake, Aneta Neumann, Markus Wagner

引用次数: 13

A new learning based dynamic multi-objective optimisation evolutionary algorithm 一种新的基于学习的动态多目标优化进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969332

Xiaogang Fu, Jianyong Sun

{"title":"A new learning based dynamic multi-objective optimisation evolutionary algorithm","authors":"Xiaogang Fu, Jianyong Sun","doi":"10.1109/CEC.2017.7969332","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969332","url":null,"abstract":"Solving dynamic multi-objective optimisation problem means to search adaptively for the Pareto optimal solutions when the environment changes. It is important to find out the changing pattern for the efficiency of the evolutionary search. Learning techniques are thus widely used to explore the dependence structure of the changing for population re-initialisation in the evolutionary search paradigm. The learning techniques are expected to discover some useful knowledge from history information, while the learned knowledge can help improve the search speed through good initialisation when change occurs. In this paper, we propose a new learning strategy based on the incorporation of mutual information, stable matching strategy and Newton's laws of motion. Mutual information is used to identify the relationship between previously found solutions; the stable matching strategy is used to associate previous found solutions bijectively and Newton's Laws of motion is applied to re-initialise the new population. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128074674","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: 3

Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement? 不同IEEE CEC竞赛获奖者对实际参数优化的分析:是否总是有改进?

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969392

D. Molina, F. Moreno-García, F. Herrera

引用次数: 8

Operating system fingerprinting via automated network traffic analysis 操作系统指纹通过自动网络流量分析

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969609

A. Aksoy, S. Louis, M. H. Gunes

引用次数: 19

A decomposition based multiobjective evolutionary algorithm with semi-supervised classification 基于分解的半监督分类多目标进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969391

Xiaoji Chen, C. Shi, Aimin Zhou, Bin Wu, Zixing Cai

{"title":"A decomposition based multiobjective evolutionary algorithm with semi-supervised classification","authors":"Xiaoji Chen, C. Shi, Aimin Zhou, Bin Wu, Zixing Cai","doi":"10.1109/CEC.2017.7969391","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969391","url":null,"abstract":"In multiobjective evolutionary algorithms, how to select the optimal solutions from the offspring candidate set significantly affects the optimization process. Usually, the selection process is largely based on the real objective values or surrogate model estimating objective values. However, these selection processes are very time consuming sometimes, especially for some real optimization problems. Recently, some researches began to employ supervised classification to assist offspring selection, but these works are difficult to prepare the exact positive and negative samples or time consuming of parameter tuning problems. In order to solve these disadvantages, we propose a decomposition based multiobjective evolutionary algorithm with semi-supervised classification. This approach using random sampling and non-dominated sorting to construct semi supervised classifier. In each generation, a set of candidate solutions are generated for each subproblem and only good solutions are reserved by classifier. If there is more than one good solutions, we calculate each of good solutions by real objective function and choose the best one as the offspring solution. Based on the typical decomposition based multiobjective evolutionary algorithm MOEA/D, we design algorithm framework through integrating the novel offspring selection process based on semi-supervised classification. Experiments show that the proposed algorithm performs best in most test cases and improves the performance of MOEA/D.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131538154","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

Landscape estimation of decision-tree induction based on grammatical Evolution using rank correlation 基于等级相关的语法演化决策树归纳的景观估计

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969389

Keiko Ono, J. Kushida

{"title":"Landscape estimation of decision-tree induction based on grammatical Evolution using rank correlation","authors":"Keiko Ono, J. Kushida","doi":"10.1109/CEC.2017.7969389","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969389","url":null,"abstract":"The type of evolutionary machine learning known as grammatical Evolution (GE) is currently receiving a great deal of attention. GE is particularly suitable for developing decision-tree classifiers because of a framework, in which candidate solutions are generated via production rules. Various decision-tree classifier methods based on GE have been proposed. In general, the performance of GE systems is improved by enhancing the genetic diversity of the candidate solutions. Therefore, most GE methods are focused on the initialization of solutions. However, it is known that an effective search bias based on a landscape is also essential for evolutionary computation methods. Unfortunately, because of their solution structures, GE-based decision-tree classifiers can not form a unique landscape in terms of an objective function as can real-valued optimization problems. In this paper, we present a method for estimating a landscape using rank correlation based on two types of features extracted from GE solutions, and we apply it to well-known benchmark problems. We show that the proposed method can capture a landscape effectively. To the best of the authors' knowledge, this is the first study to report about a landscape estimation method based on GE solutions. The results in this paper help with understanding how to establish suitable a search bias for GE-based decision-tree classifiers.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131872572","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: 1

Configuring advanced evolutionary algorithms for multicriteria building spatial design optimisation 为多准则建筑空间设计优化配置先进的进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969520

K. Blom, S. Boonstra, H. Hofmeyer, Thomas Bäck, M. Emmerich

引用次数: 11