The Experts below are selected from a list of 8526 Experts worldwide ranked by ideXlab platform
Zhipeng Lu - One of the best experts on this subject based on the ideXlab platform.
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a tabu search based Memetic Algorithm for the maximum diversity problem
Engineering Applications of Artificial Intelligence, 2014Co-Authors: Yang Wang, Fred Glover, Zhipeng LuAbstract:This paper presents a highly effective Memetic Algorithm for the maximum diversity problem based on tabu search. The tabu search component uses a successive filter candidate list strategy and the solution combination component employs a combination operator based on identifying strongly determined and consistent variables. Computational experiments on three sets of 40 popular benchmark instances indicate that our tabu search/Memetic Algorithm (TS/MA) can easily obtain the best known results for all the tested instances (where no previous Algorithm has achieved) as well as improved results for six instances. Analysis of comparisons with state-of-the-art Algorithms demonstrates statistically that our TS/MA competes very favorably with the best performing Algorithms. Key elements and properties of TS/MA are also analyzed to disclose the benefits of integrating tabu search (using a successive filter candidate list strategy) and solution combination (based on critical variables).
Tao Xiong - One of the best experts on this subject based on the ideXlab platform.
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a pso and pattern search based Memetic Algorithm for svms parameters optimization
Neurocomputing, 2013Co-Authors: Zhongyi Hu, Tao XiongAbstract:Abstract Addressing the issue of SVMs parameters optimization, this study proposes an efficient Memetic Algorithm based on particle swarm optimization Algorithm (PSO) and pattern search (PS). In the proposed Memetic Algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with optimum solutions, while pattern search (PS) is used to produce an effective exploitation on the potential regions obtained by PSO. Moreover, a novel probabilistic selection strategy is proposed to select the appropriate individuals among the current population to undergo local refinement, keeping a well balance between exploration and exploitation. Experimental results confirm that the local refinement with PS and our proposed selection strategy are effective, and finally demonstrate the effectiveness and robustness of the proposed PSO-PS based MA for SVMs parameters optimization.
Angela Hsiangling Chen - One of the best experts on this subject based on the ideXlab platform.
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a Memetic Algorithm with a variable block insertion heuristic for single machine total weighted tardiness problem with sequence dependent setup times
Congress on Evolutionary Computation, 2016Co-Authors: Fatih M Tasgetiren, Yucel Ozturkoglu, Angela Hsiangling ChenAbstract:In this paper, a Memetic Algorithm with a variable block insertion heuristic is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. Together with the traditional insertion neighborhood structure, the Memetic Algorithm is combined with a variable block insertion heuristic in which a block of jobs are removed from a sequence and then inserted into all possible positions of the partial sequence. For this purpose, we devise a variable neighborhood descent Algorithm to incorporate different block insertion heuristics having different block sizes. We also employ a simulated annealing type of acceptance criterion to diversify the population. To evaluate its performance, the Memetic Algorithm is tested on a set of benchmark instances from the literature. The analyses of experimental results have shown highly effective performance of the Memetic Algorithm against the best performing Algorithms from the literature. The proposed Memetic Algorithm was able to find 98 out 120 optimal solutions within reasonable CPU times.
Yang Wang - One of the best experts on this subject based on the ideXlab platform.
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a tabu search based Memetic Algorithm for the maximum diversity problem
Engineering Applications of Artificial Intelligence, 2014Co-Authors: Yang Wang, Fred Glover, Zhipeng LuAbstract:This paper presents a highly effective Memetic Algorithm for the maximum diversity problem based on tabu search. The tabu search component uses a successive filter candidate list strategy and the solution combination component employs a combination operator based on identifying strongly determined and consistent variables. Computational experiments on three sets of 40 popular benchmark instances indicate that our tabu search/Memetic Algorithm (TS/MA) can easily obtain the best known results for all the tested instances (where no previous Algorithm has achieved) as well as improved results for six instances. Analysis of comparisons with state-of-the-art Algorithms demonstrates statistically that our TS/MA competes very favorably with the best performing Algorithms. Key elements and properties of TS/MA are also analyzed to disclose the benefits of integrating tabu search (using a successive filter candidate list strategy) and solution combination (based on critical variables).
Dalila Boughaci - One of the best experts on this subject based on the ideXlab platform.
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a Memetic Algorithm with support vector machine for feature selection and classification
Memetic Computing, 2015Co-Authors: Messaouda Nekkaa, Dalila BoughaciAbstract:The Memetic Algorithm (MA) is an evolutionary metaheuristic that can be viewed as a hybrid genetic Algorithm combined with some kinds of local search. In this paper, we propose a Memetic Algorithm combined with a support vector machine (SVM) for feature selection and classification in Data mining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. In addition, another hybrid Algorithm of MA and SVM with optimized parameters is also developed. The two versions of our proposed method are evaluated on some datasets and compared with some well-known classifiers for data classification. The computational experiments show that the hybrid method MA + SVM with optimized parameters provides competitive results and finds high quality solutions.
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a Memetic Algorithm for the optimal winner determination problem
Soft Computing, 2009Co-Authors: Dalila Boughaci, Belaid Benhamou, Habiba DriasAbstract:In this paper, we propose a Memetic Algorithm for the optimal winner determination problem in combinatorial auctions. First, we investigate a new selection strategy based on both fitness and diversity to choose individuals to participate in the reproduction phase of the Memetic Algorithm. The resulting Algorithm is enhanced by using a stochastic local search (SLS) component combined with a specific crossover operator. This operator is used to identify promising search regions while the stochastic local search performs an intensified search of solutions around these regions. Experiments on various realistic instances of the considered problem are performed to show and compare the effectiveness of our approach.
