Abstract
A number of modern hybrid genetic algorithms do not use mutation. Instead, these algorithms use local search to improve intermediate solutions. This same strategy of combining local search and crossover is also used by stochastic local algorithms, such the LKH heuristic for the Traveling Salesman Problem. We prove that a simple hybrid genetic algorithm that uses only local search and a form of deterministic “voting crossover” can solve the well known Jump Function in \(\varTheta (n)\) time where the jump distance is log(n).
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Acknowledgements
This work was supported by a grant from the US National Science Foundation CISE/ACE, SSI-SI2. Dr. Mukhopadhyay was supported by a Fulbright-Nehru Academic and Professional Excellence Fellowship.
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Whitley, D., Varadarajan, S., Hirsch, R., Mukhopadhyay, A. (2018). Exploration and Exploitation Without Mutation: Solving the Jump Function in \(\varTheta (n)\) Time. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11102. Springer, Cham. https://doi.org/10.1007/978-3-319-99259-4_5
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