HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-K Weight Clique Search Problem
Abstract
The diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with extensive applications, which extends the DTKC search problem by taking into account the weight of vertices. In this paper, we formulate DTKWC search problem using mixed integer linear program constraints and propose an efficient hybrid evolutionary algorithm (HEA-D) that combines a clique-based crossover operator and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The experimental results show that HEA-D performs much better than the existing methods on two representative real-world benchmarks.
Cite
Text
Wu et al. "HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-K Weight Clique Search Problem." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/668Markdown
[Wu et al. "HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-K Weight Clique Search Problem." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/wu2022ijcai-hea/) doi:10.24963/IJCAI.2022/668BibTeX
@inproceedings{wu2022ijcai-hea,
title = {{HEA-D: A Hybrid Evolutionary Algorithm for Diversified Top-K Weight Clique Search Problem}},
author = {Wu, Jun and Li, Chu Min and Zhou, Yupeng and Yin, Minghao and Xu, Xin and Niu, Dangdang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4821-4827},
doi = {10.24963/IJCAI.2022/668},
url = {https://mlanthology.org/ijcai/2022/wu2022ijcai-hea/}
}