Hierarchical evolution of linear regressors

Authors:
Francesc Teixidó-Navarro;Albert Orriols-Puig;Ester Bernadó-Mansilla
Affiliations:
Universitat Ramon Llull, Barcelona, Spain;Universitat Ramon Llull, Barcelona, Spain;Universitat Ramon Llull, Barcelona, Spain
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learning approach. A genetic algorithm is iteratively called to find a partition of the search space where a linear regressor can accurately fit the objective function. The resulting ruleset performs an approximation to the objective function formed by a hierarchy of locally trained linear regressors. The approach is evaluated in a set of objective functions and compared to other regression techniques.