A parallel solution to the HIP game based on genetic algorithms

Authors:
Tatiana Tambouratzis
Affiliations:
Department of Industrial Management & Technology, University of Piraeus, Piraeus, Greece
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

In this piece of research, genetic algorithms are put forward for solving the HIP game. The proposed parallel approach manipulates candidate solutions via selection and mutation; no crossover has been employed. The population is limited to one candidate solution per generation, thus keeping the computational complexity of the approach to a minimum. It is shown that the proposed approach is superior to the approaches reported in the literature: solutions are more speedily provided while the frequency of finding a solution is significantly higher.