Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks
Dongxiao He, Zhe Wang, Bin Yang, and Chunguang Zhou

TL;DR
This paper introduces GAEL, a genetic algorithm enhanced with ensemble learning and local search strategies, to improve community detection accuracy in complex networks, outperforming existing methods.
Contribution
The paper presents a novel genetic algorithm with ensemble learning-based crossover and local search, specifically designed for community detection in complex networks.
Findings
GAEL outperforms existing community detection algorithms.
Ensemble learning-based crossover improves genetic recombination.
Markov random walk initialization enhances solution diversity.
Abstract
Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual crossover operator based on ensemble learning. Therefore, GAEL can avoid the problems that are brought by traditional crossover operator which is only able to mix string blocks of different individuals, but not able to recombine clustering contexts of different individuals into new better ones. In addition, the local search strategy, which makes mutated node be placed into the community where most of its neighbors are, is used in mutation operator. At last, a Markov random walk based method is…
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