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Adaptive strategies for a semantically driven tree optimizer to control code growth

, and . GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2, page 1762--1762. London, ACM Press, (7-11 July 2007)

Abstract

In genetic programming many methods to fight growth exist. But most of these methods require one or multiple parameters to be set. Unfortunately performance strongly depends on a correct setting of each of those parameters. Recently a semantically driven tree optimiser has been developed. In this paper two adaptive strategies to choose a reasonable parameter setting for this growth limiter are presented.

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