An Efficient Evolutionary Algorithm for Diversified Top-k (Weight) Clique Search Problems
CoRR(2024)
摘要
In many real-world problems and applications, finding only a single element,
even though the best, among all possible candidates, cannot fully meet the
requirements. We may wish to have a collection where each individual is not
only outstanding but also distinctive. Diversified Top-k (DTk) problems are a
kind of combinatorial optimization problem for finding such a promising
collection of multiple sub-structures, such as subgraphs like cliques and
social communities. In this paper, we address two representative and practical
DTk problems, DTk Clique search (DTkC) and DTk Weight Clique search (DTkWC),
and propose an efficient algorithm called Diversified Top-k Evolutionary
AlgorithM (DiverTEAM) for these two problems. DiverTEAM consists of a local
search algorithm, which focuses on generating high-quality and diverse
individuals and sub-structures, and a genetic algorithm that makes individuals
work as a team and converge to (near-)optima efficiently. Extensive experiments
show that DiverTEAM exhibits an excellent and robust performance across various
benchmarks of DTkC and DTkWC.
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