Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm

This paper presents a scaling analysis of the ordering messy genetic algorithm (OmeGA), a fast messy genetic algorithm that uses random keys to represent solutions. In experiments with hard permutation problems--so-called ordering deceptive problems--it is shown that the algorithm scales up as O(l1.4) with the problem length l ranging from 32 to 512. Moreover, the OmeGA performs efficiently with small populations thereby consuming little memory. Since the algorithm is independent of the structure of the building blocks, it outperforms the random key-based simple genetic algorithm (RKGA) for loosely coded problems.

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