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The minimum conductance problem is an NP-hard graph partitioning problem.Apart from the search for bottlenecks in complex networks, the problem is veryclosely related to the popular area of network community detection. In thispaper, we tackle the minimum conductance problem as a pseudo-Booleanoptimisation problem and propose a memetic algorithm to solve it. An efficientlocal search strategy is established. Our memetic algorithm starts by usingthis local search strategy with different random strings to sample a set ofdiverse initial solutions. This is followed by an evolutionary phase based on asteady-state framework and two intensification subroutines. We compare thealgorithm to a wide range of multi-start local search approaches and classicalgenetic algorithms with different crossover operators. The experimental resultsare presented for a diverse set of real-world networks. These results indicatethat the memetic algorithm outperforms the alternative stochastic approaches.

A Memetic Algorithm for the Minimum Conductance Graph Partitioning Problem