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Greedy Semantic Local Search for Small Solutions

Robyn Ffrancon 1 Marc Schoenauer 1 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal " should-be " values each subtree should return, whilst assuming that the rest of the tree is unchanged, and to choose a subtree that matches as well as possible these target values. A single tree is evolved by iteratively replacing one of its nodes with the best subtree from a static library according to this local fitness, with tree size as a secondary criterion. Previous results for standard Boolean GP benchmarks that have been obtained by the authors with another variant of SB are improved in term of tree size. SB is then applied for the first time to categorical GP benchmarks, and outperforms all known results to date for three variable finite algebras.
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Submitted on : Tuesday, June 30, 2015 - 2:56:45 PM
Last modification on : Saturday, June 25, 2022 - 10:16:46 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 7:15:53 PM


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  • HAL Id : hal-01169075, version 1


Robyn Ffrancon, Marc Schoenauer. Greedy Semantic Local Search for Small Solutions. Companion Proceedings (workshops) of the Genetic and Evolutionary Computation COnference, Jul 2015, TAO, INRIA Saclay, France. pp.1293-1300. ⟨hal-01169075⟩



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