Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
Discovering approximate expressions of GPS geometric dilution of precision using genetic programming
Advances in Engineering Software
Accelerated parallel genetic programming tree evaluation with OpenCL
Journal of Parallel and Distributed Computing
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In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are summarized in this paper.