A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
A review of tournament selection in genetic programming
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Genetic programming with a norm-referenced fitness function
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Selection pressure must be dynamically managed in response to the changing evolutionary process in order to improve the effectiveness and efficiency of Genetic Programming (GP) systems using tournament selection. Instead of changing the tournament size and/or the population size via an arbitrary function to influence the selection pressure, this paper focuses on designing an automatic selection pressure control approach. In our approach, populations are clustered based on a dynamic program property. Then clusters become tournament candidates. The selection pressure in the tournament selection method is automatically changed during evolution according to the dynamically changing number of tournament candidates. Our approach is compared with the standard GP system (with no selection pressure control) on two problems with different kinds of fitness distributions. The results show that the automatic selection pressure control approach can improve the effectiveness and efficiency of GP systems.