Gene expression programming based on simulated annealing

Gene expression programming (GEP) is a genotype/phenotype system that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length. However, the performance of basic GEP is highly dependent on the genetic operators´ rate. In this work, we present a new algorithm called GEPSA that combines GEP and simulated annealing (SA), and GEPSA decreases the dependence on genetic operators´ rate without impairing the performance of GEP. Three function finding problems, including a benchmark problem of prediction sunspots, are tested on GEPSA, results shows that importing simulated annealing can improve the performance of GEP.