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.
- J. Branke. Memory enhanced evolutionary algorithms for changing optimization problems. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 3. IEEE, 1999.Google ScholarCross Ref
- I. Dempsey, M. O'Neill, and A. Brabazon. Adaptive trading with grammatical evolution. In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 2587--2592. IEEE, 2006.Google ScholarCross Ref
- I. Dempsey, M. O'Neill, and A. Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer Verlag, 2009. Google ScholarCross Ref
- J. McDermott et al. Genetic programming needs better benchmarks. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, pages 791--798. ACM, 2012. Google ScholarDigital Library
- I. Moser and R. Chiong. Dynamic function optimization: The moving peaks benchmark. Metaheuristics for Dynamic Optimization, pages 35--59, 2013.Google ScholarCross Ref
- N. Wagner, Z. Michalewicz, M. Khouja, and R. McGregor. Time series forecasting for dynamic environments: the dyfor genetic program model. Evolutionary Computation, IEEE Transactions on, 11(4):433--452, 2007. Google ScholarDigital Library
Index Terms
- Towards a dynamic benchmark for genetic programming
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