A Hybrid Evolutionary Algorithm for the Diversified Top-k Weight Clique Search Problem (Student Abstract)
Keywords:Diversified Top-$k$ Weight Clique, MILP, Metaheuristics, Hybrid Evolutionary Search, Crossover, Local Optimization
AbstractThe diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique search problem, which extends the DTKC search problem by taking into account the weight of vertices. This problem involves finding at most k maximal weighted cliques that cover maximum weight of vertices with low overlapping in a given graph. In this study, a mixed integer linear program constraint formulation is proposed to model DTKWC search problem and an efficient hybrid evolutionary algorithm (HEA-D) based on some heuristic strategies is proposed to tackle it. Experiments on two sets of 110 graphs show that HEA-D outperforms the state-of-art methods.
How to Cite
Wu, J., & Yin, M. (2022). A Hybrid Evolutionary Algorithm for the Diversified Top-k Weight Clique Search Problem (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13083-13084. https://doi.org/10.1609/aaai.v36i11.21678
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