2022
DOI: 10.1016/j.knosys.2022.109986
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A deep learning guided memetic framework for graph coloring problems

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Cited by 15 publications
(5 citation statements)
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“…When we look at the frequencies of the local search operators selected by AHEAD, we see that TabuCol is selected most often in comparison with PartialCol, which is no surprise, as TabuCol is already better on its own for a greater number of instances. However, when it comes to crossovers, we observe a balanced choice between the three GPX variants, with a bias toward the more conservative crossover GPX-9 for geometric graphs (e.g., DSJR500.5) and sparse graphs (e.g., wap instances), for which local optima are very distant in the search space, while the GPX crossover is more often preferred for random and dense graphs (e.g., DSJC1000.9), for which there is often larger backbones of solutions shared by the high-quality solutions (as shown in [10]).…”
Section: Detailed Resultsmentioning
confidence: 99%
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“…When we look at the frequencies of the local search operators selected by AHEAD, we see that TabuCol is selected most often in comparison with PartialCol, which is no surprise, as TabuCol is already better on its own for a greater number of instances. However, when it comes to crossovers, we observe a balanced choice between the three GPX variants, with a bias toward the more conservative crossover GPX-9 for geometric graphs (e.g., DSJR500.5) and sparse graphs (e.g., wap instances), for which local optima are very distant in the search space, while the GPX crossover is more often preferred for random and dense graphs (e.g., DSJC1000.9), for which there is often larger backbones of solutions shared by the high-quality solutions (as shown in [10]).…”
Section: Detailed Resultsmentioning
confidence: 99%
“…The architecture of the AHEAD framework, illustrated in Figure 1, extends the state-of-the-art HEAD framework [21] by introducing an operator selector. In particular, its simplicity with only two individuals in the population facilitates the parent matching and population updating phases compared to other memetic algorithms in the literature [7,18,24,10]. AHEAD takes as input a graph G = (V, E), and an integer value k for the kcol or a weight function w for the WVCP, a set of local search operators O l , a set of crossover operators O x , and a high-level selection strategy π θ characterized by a parameter vector θ.…”
Section: Main Schemementioning
confidence: 99%
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“…Lü and Hao introduced a memetic algorithm (MACOL) to address GCP [5]. Goudet et al proposed a memetic framework guided by deep learning for graph coloring problems and implemented it on GPU devices to solve the classical vertex k-coloring problem and the weighted vertex coloring problem [6]. In a related study, Marappan et al concentrated on developing a new Particle Swarm Optimization (PSO) model that minimizes the search space and number of generations required.…”
Section: Literature Reviewmentioning
confidence: 99%