We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluations and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller solutions. We also consider the effect of pool size and the choice of fitness function on convergence and total computation.

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In Riolo, R., Worzel, W., Kotanchek, M., editors, Genetic Programming Theory and Practice XII, University of Michigan, Ann Arbor, USA, May 2015. Springer International Publishing Switzerland.

@inproceedings{shahrzad:gptp15, title={Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System}, author={Hormoz Shahrzad and Babak Hodjat}, booktitle={Genetic Programming Theory and Practice XII}, month={May}, editor={Riolo, R., Worzel, W., Kotanchek, M.}, address={University of Michigan, Ann Arbor, USA}, publisher={Springer International Publishing Switzerland}, url="http://nn.cs.utexas.edu/?shahrzad:gptp15", year={2015} }