A memetic algorithm for the Minimum Sum Coloring Problem

Jin, Yan; Hao, Jin‐Kao; Hamiez, Jean-Philippe

doi:10.1016/j.cor.2013.09.019

Cited by 34 publications

(26 citation statements)

References 18 publications

(27 reference statements)

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“…They combine EAs to explore the solution space with local refinement procedures applied for expoiting the solutions already found [18]. Due to their high convergence capabilities and wide practical applicability, a number of MAs have been proposed for solving various pattern recognition and optimization problems [8,9,15,17,20].…”

Section: Related Literaturementioning

confidence: 99%

A memetic algorithm to select training data for support vector machines

Nalepa

Kawulok

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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In this paper we propose a new memetic algorithm (MASVM) for fast and efficient selection of a valuable training set for support vector machines (SVMs). This is a crucial step especially in case of large and noisy data sets, since the SVM training has high time and memory complexity. The majority of state-of-the-art methods exploit the data geometry analysis, both in the input and kernel space. Although evolutionary algorithms have been proven to be very efficient for this purpose, they have not been extensively studied so far. Here, we propose a new method employing an adaptive genetic algorithm enhanced by some refinement techniques. The refinements are based on utilizing a pool of the support vectors identified so far at various steps of the algorithm. Extensive experimental study performed on the well-known benchmark, real-world and artificial data sets clearly confirms the efficacy, robustness and convergence capabilities of the proposed approach, and shows that it is competitive compared with other state-of-the-art techniques.

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Section: Related Literaturementioning

confidence: 99%

A memetic algorithm to select training data for support vector machines

Nalepa

Kawulok

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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“…24 As a variation the graph coloring problem, the sum coloring problem which is more complicated was also investigated. For example, Jin et al 25 proposed a memetic algorithm for the sum coloring problem; Bouziri and Jouini 26 proposed a tabu search approach for the sum coloring problem.…”

Section: Introductionmentioning

confidence: 99%

A Simulated Annealing Algorithm with Effective Local Search for Solving the Sum Coloring Problem

Zhao¹,

Shu²

2016

j comput theor nanosci

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The sum coloring problem is a variant of the vertex coloring problem where the target is to minimize the sum of colors used in coloring the vertices. It is NP-complete and has many applications in scheduling. In this paper, we use local search to solve the sum coloring problem. Extensive simulation results show that the proposed algorithm outperforms the existing tabu search algorithm, and is able to obtain satisfactory solution of many DIMACS benchmark instances.

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“…The main results on M SCP include the theoretical bounds [13,29,22,2] and the structural properties relative to the graph families for which efficient M SCP algorithms exist. Recently, heuristics and meta-heuristics for M SCP are proposed in [19] and [28,15,3,25] respectively, giving bounds for DIMACS and COLOR graphs, and exact methods are proposed in [17].…”

Section: Introductionmentioning

confidence: 99%