Geometric-based sampling for permutation optimization

There exist several operators to search through permutation spaces that can benefit search and score algorithms when combined. This paper presents COMpetitive Mutating Agents (COMMA), an algorithm which uses geometric mutation operators to create a geometrically defined distribution of solutions. Sampling from the distribution generates solutions in a similar fashion as with Estimation of Distribution Algorithms (EDAs). COMMA is applied on classical permutation optimization benchmarks, namely the Quadratic Assignement and the Permutation Flowshop Scheduling Problems and its performance is compared with those of reference EDAs. Although COMMA does not require a model building step, results suggest that it is competitive with state-of-the-art EDAs. In addition, COMMA's underlying geometric-based sampling could be transposed to representations other than permutations.

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