An Efficient New Scheme of Fitness Evaluation in Genetic Programming using the R Language

The aim of this paper is to propose and analyze several fitness evaluation schemes for solving regression problems using Genetic Programming. The proposed schemes are designed considering the particularities and characteristics of the R Language, and particularly the capacities of the language for matrix manipulation and mathematical expressions evaluation. Experimental results show that some the proposed schemes are able to reduce until 99% the time spend in fitness evaluation in comparison with the original genetic programming implementation using a traditional tree structure.