2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)最新文献_第3页

Ensemble classifiers with improved overfitting 改进过拟合的集成分类器

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482130

Z. Pourtaheri, S. Zahiri

{"title":"Ensemble classifiers with improved overfitting","authors":"Z. Pourtaheri, S. Zahiri","doi":"10.1109/CSIEC.2016.7482130","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482130","url":null,"abstract":"Overfitting has been always considered as a challenging problem in designing and training of ensemble classifiers. Obviously, the use of complex multiple classifiers may increase the success of ensemble classifier in feature space division with intertwined data and also may decrease the training error to minimum value. However, this success does not exist on the test data. Ensemble classifiers are more prone to overfitting than single classifiers because ensemble classifiers have been formed of several base classifiers and overfitting occurrence in each base classifier can transfer the problem to the final decision of the ensemble. In this paper, after quantitative and qualitative analysis of overfitting, a solution for improving overfitting is proposed by using heuristic algorithms. In this way, Multi-Objective Inclined Planes Optimization (MOIPO) and Multi-Objective Particle Swarm Optimization (MOPSO) are used and their results are compared with each other. Simulation results show that the simultaneous minimization of ensemble size and error rate in the training phase, can lead to a significant reduction in the amount of overfitting. In fact, with this approach in the training phase, the ensemble classifier is required to minimize the error with the most simple and minimum number of base classifiers and therefore overfitting is prevented. However, previous researches related to overfitting have ignored the ensemble size as an objective function.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128173892","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: 21

Flocking control with single-COM for tracking a moving target in mobile sensor network using gravitational search algorithm 基于引力搜索算法的移动传感器网络中运动目标的单com集群控制

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482114

E. Khodayari, Vahid Sattari-Naeini, M. Mirhosseini

引用次数: 2

A multi-agent system approach to control road transportation network 道路运输网络控制的多智能体系统方法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482113

Mortaza zolfpour-Arokhlo, M. Mashinchi

引用次数: 4

A hybrid method for dimensionality reduction in microarray data based on advanced binary ant colony algorithm 基于先进二元蚁群算法的微阵列数据混合降维方法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482124

A. Rouhi, H. Nezamabadi-pour

引用次数: 15

A new metaheuristic football game inspired algorithm 一种新的启发式足球游戏算法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482120

E. Fadakar, M. Ebrahimi

{"title":"A new metaheuristic football game inspired algorithm","authors":"E. Fadakar, M. Ebrahimi","doi":"10.1109/CSIEC.2016.7482120","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482120","url":null,"abstract":"Metaheuristics are high level strategies for exploring the search space by using different methods to solve global optimization problems. In this paper, Football Game Algorithm has been proposed as a new metaheuristic algorithm based on the simulation of football players' behavior during a game for finding best positions to score a goal under supervision of the team coach. Simulation of humans' intelligences who are working together as a team to reach a specific goal instead of simulating the intelligence of various animal swarms in the nature is the most important distinction of the proposed algorithm to other existing algorithms that also introduces a new approach for making balance between diversification and intensification. Football Game Algorithm is a nature inspired, population base algorithm with ability in finding multiple global optimums. We have studied general football game tactics and idealized its characteristics to formulate Football Game Algorithm. We have then compared the proposed algorithm with other metaheuristics, including standard and modified particle swarm optimization and bat algorithm. The result of comparison studies show that the proposed Algorithm outperforms other algorithms and also has more robust performance. Finally, we have discussed and concluded by pointing out special attributes of the Football Game Algorithm.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127702895","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: 42

Improved genetic algorithm using chaotic cellular automata — CCAGA 基于混沌元胞自动机的改进遗传算法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482112

Ehsan Tafehi, S. Ahmadnia, M. Yousefi

{"title":"Improved genetic algorithm using chaotic cellular automata — CCAGA","authors":"Ehsan Tafehi, S. Ahmadnia, M. Yousefi","doi":"10.1109/CSIEC.2016.7482112","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482112","url":null,"abstract":"Genetic Algorithm (GA) is a search technique used to find the optimized solution in a problem space. The main problem in using the GA is in complex multi-peak search problems that usually leads to premature convergence. Furthermore certain optimization problems, such as variant problems, cannot be solved by means of genetic algorithms. This occurs due to poorly known fitness functions which generate bad chromosome blocks in spite of the fact that only good chromosome blocks crossover. In this paper by using Chaotic Cellular Automata (CCA) along with influencing Pseudo Random Number Generator (PRN), a new and enhanced method for GA is presented. Mutation, crossover and elitism's percentage selection are all influenced by Pseudo Random Number Generator (PRNG), and consequently, chaotic numbers are produced which completely change the GA performance. Mentioned factors lead to the appropriate random behavior for the genome in the problem space which give the GA, high exploitation and exploration ability. Moreover this unpredictable behavior in changing the GA's factors with the percentage of elitism selection created by CCA, help the proposed algorithm to avoid converging prematurely and falling in local minimums as well as the ability to cover the bigger space's problem. In comparison with traditional GA algorithm, the proposed method illustrates faster and accurate performance for searching in problem space with more exploration ability.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129533866","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

An optimal SVM with feature selection using multi-objective PSO 基于多目标粒子群算法的最优支持向量机特征选择

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1155/2016/6305043

I. Behravan, S. Zahiri, Oveis Dehghantanha

{"title":"An optimal SVM with feature selection using multi-objective PSO","authors":"I. Behravan, S. Zahiri, Oveis Dehghantanha","doi":"10.1155/2016/6305043","DOIUrl":"https://doi.org/10.1155/2016/6305043","url":null,"abstract":"Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM, depends on different parameters such as: penalty factor, C, and the kernel factor, o. Also choosing an appropriate kernel function can improve the Recognition Score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computation time and complexity. So this is an optimization problem which can be solved by a heuristic algorithm. In some cases besides the Recognition Score, the Reliability of the classifier's output, is important. So in such cases a multi-objective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function and select the best features simultaneously in order to optimize the Recognition Score and the Reliability of the SVM. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM). The results of the proposed method are compared to those which are achieved by RBF and MLP neural networks.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130762058","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: 21

A parallel solution for the 0–1 knapsack problem using firefly algorithm 用萤火虫算法并行求解0-1背包问题

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482134

Mohammad Hajarian, A. Shahbahrami, F. Hoseini

引用次数: 8

Load balanced energy-aware genetic algorithm clustering in wireless sensor networks 无线传感器网络中负载均衡能量感知遗传算法聚类

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482108

Ebrahim Farahmand, Saeideh Sheikhpour, A. Mahani, N. Taheri

{"title":"Load balanced energy-aware genetic algorithm clustering in wireless sensor networks","authors":"Ebrahim Farahmand, Saeideh Sheikhpour, A. Mahani, N. Taheri","doi":"10.1109/CSIEC.2016.7482108","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482108","url":null,"abstract":"Extending lifetime of wireless sensor networks is a major issue in WSNs due to their energy resource constraint. To solve this problem, various approaches have been proposed recently. Clustering is an effective topology control technique, among these approaches. This paper introduces a novel Load balanced Energy-aware Genetic Algorithm Clustering (LEGAC) technique applied in WSNs. This technique is implemented at the base station. In the proposed technique, two-stage GA is employed to select optimal set of clusters. In the first stage, the technique picks up the optimal cluster heads. In the second stage, this technique assigns appropriate cluster members to these cluster heads. Moreover, intra-cluster distance is optimized tacking into account load-balancing constraint. The objective is to minimize energy consumption. The performance of this technique is compared with similar techniques, i.e., UCFIA-unequal clustering algorithm using Fuzzy logic, GCA-multi-hop clustering, and SCP-load balanced staggered clustering protocol. Simulation results show that LEGAC outperforms all these techniques in the same set up in terms of network lifetime, energy efficiency and network coverage.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129859696","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

A clustering algorithm based on integration of K-Means and PSO 基于K-Means和粒子群算法的聚类算法

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482110

H. Atabay, Mohammad Javad Sheikhzadeh, M. Torshizi

引用次数: 17