Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System

Chapter
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

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.

Keywords

Evolutionary computation Genetic algorithms Genetic programming Fitness functions 

Notes

Acknowledgements

The authors wish to thank Sentient Technologies Holdings Limited for sponsoring this research and providing the processing capacity required for some of the experiments presented in this paper.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Sentient Technologies Holdings LimitedSan FranciscoUSA

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