The Experts below are selected from a list of 168024 Experts worldwide ranked by ideXlab platform
Sam Kwong - One of the best experts on this subject based on the ideXlab platform.
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agent based Evolutionary Approach for interpretable rule based knowledge extraction
Systems Man and Cybernetics, 2005Co-Authors: Hanli Wang, Sam KwongAbstract:An agent-based Evolutionary Approach is proposed to extract interpretable rule-based knowledge. In the multiagent system, each fuzzy set agent autonomously determines its own fuzzy sets information, such as the number and distribution of the fuzzy sets. It can further consider the interpretability of fuzzy systems with the aid of hierarchical chromosome formulation and interpretability-based regulation method. Based on the obtained fuzzy sets, the Pittsburgh-style Approach is applied to extract fuzzy rules that take both the accuracy and interpretability of fuzzy systems into consideration. In addition, the fuzzy set agents can cooperate with each other to exchange their fuzzy sets information and generate offspring agents. The parent agents and their offspring compete with each other through the arbitrator agent based on the criteria associated with the accuracy and interpretability to allow them to remain competitive enough to move into the next population. The performance with emphasis upon both the accuracy and interpretability based on the agent-based Evolutionary Approach is studied through some benchmark problems reported in the literature. Simulation results show that the proposed Approach can achieve a good tradeoff between the accuracy and interpretability of fuzzy systems.
Prasanta K Jana - One of the best experts on this subject based on the ideXlab platform.
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a novel Evolutionary Approach for load balanced clustering problem for wireless sensor networks
Swarm and evolutionary computation, 2013Co-Authors: Pratyay Kuila, Suneet Kumar Gupta, Prasanta K JanaAbstract:Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximizing lifetime of Wireless Sensor Networks (WSNs). However, in a cluster based WSN, the leaders (cluster heads) bear some extra load for various activities such as data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. Load balanced clustering is known to be an NP-hard problem for a WSN with unequal load of the sensor nodes. Genetic Algorithm (GA) is one of the most popular Evolutionary Approach that can be applied for finding the fast and efficient solution of such problem. In this paper, we propose a novel GA based load balanced clustering algorithm for WSN. The proposed algorithm is shown to perform well for both equal as well as unequal load of the sensor nodes. We perform extensive simulation of the proposed method and compare the results with some Evolutionary based Approaches and other related clustering algorithms. The results demonstrate that the proposed algorithm performs better than all such algorithms in terms of various performance metrics such as load balancing, execution time, energy consumption, number of active sensor nodes, number of active cluster heads and the rate of convergence.
Florez-revuelta Francisco - One of the best experts on this subject based on the ideXlab platform.
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EvoSplit: An Evolutionary Approach to split a multi-label data set into disjoint subsets
'MDPI AG', 2021Co-Authors: Florez-revuelta FranciscoAbstract:This paper presents a new Evolutionary Approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective Evolutionary Approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective Evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both Approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples
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EvoSplit: An Evolutionary Approach to split a multi-label data set into disjoint subsets
2021Co-Authors: Florez-revuelta FranciscoAbstract:This paper presents a new Evolutionary Approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective Evolutionary Approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective Evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (label and label pair). Both Approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.Comment: This work has been submitted to a journal for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
Moez Davodi - One of the best experts on this subject based on the ideXlab platform.
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reliability based generator maintenance scheduling using hybrid Evolutionary Approach
International Journal of Electrical Power & Energy Systems, 2012Co-Authors: Ehsan Reihani, Ali Sarikhani, Moez DavodiAbstract:Abstract With the growth of electrical energy demand, providing reliable energy without interruption has become very important nowadays. Maintenance scheduling of generating units is one of the crucial factors in delivering reliable electrical energy to the vital industrial and urban loads. As number of generating units and constraints over their operation is increasing, there is growing need for developing new methods for planning optimal outage of generating units for maintenance. This paper presents a hybrid Evolutionary algorithm to tackle the reliability based generator maintenance scheduling problem. Uncertainties in the generating units and the load variations are included so that a more realistic scheduling is obtained. Maintenance scheduling problem is a large scale constrained optimization problem with a large number of variables which needs novel methods to cope with it. A new local search method which is derived from Extremal Optimization (EO) and Genetic Algorithm (GA) is presented to tackle the problem. The proposed method can be used as a local optimizer to further improve the potential solutions in the GA. The proposed method, Hill Climbing Technique (HCT), GA and their hybrid Approaches are applied to the IEEE Reliability Test System (RTS) and the obtained results are discussed.
Barry Mccollum - One of the best experts on this subject based on the ideXlab platform.
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a hybrid Evolutionary Approach to the university course timetabling problem
Congress on Evolutionary Computation, 2007Co-Authors: Salwani Abdullah, Edmund K Burke, Barry MccollumAbstract:Combinations of Evolutionary based Approaches with local search have provided very good results for a variety of scheduling problems. This paper describes the development of such an algorithm for university course timetabling. This problem is concerned with the assignment of lectures to specific timeslots and rooms. For a solution to be feasible, a number of hard constraints must be satisfied. The quality of the solution is measured in terms of a penalty value which represents the degree to which various soft constraints are satisfied. This hybrid Evolutionary Approach is tested over established datasets and compared against state-of-the-art techniques from the literature. The results obtained confirm that the Approach is able to produce solutions to the course timetabling problem which exhibit some of the lowest penalty values in the literature on these benchmark problems. It is therefore concluded that the hybrid Evolutionary Approach represents a particularly effective methodology for producing high quality solutions to the university course timetabling problem.