Towards a dynamic benchmark for genetic programming

Authors:
Cliodhna Tuite;Michael O'Neill;Anthony Brabazon
Affiliations:
Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland;Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland;Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

Citing 3
Cited 0

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Genetic programming needs better benchmarks

Proceedings of the 14th annual conference on Genetic and evolutionary computation

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Abstract

Following a recent call for a suite of benchmarks for genetic programming, we investigate the criteria for a meaningful dynamic benchmark for GP. We explore the design of a dynamic benchmark for symbolic regression, based on semantic distance between evaluated functions, where larger distances serve as a proxy for greater environmental change. We do not find convincing evidence that lower semantic distance is a good proxy for greater ease in adapting to a change. We conclude that due to fundamental characteristics of GP, it is difficult to come up with a single dynamic benchmark problem which is generally applicable.