Tuning Genetic Programming parameters with factorial designs

Parameter setting of Evolutionary Algorithms is a time consuming task with two main approaches: parameter tuning and parameter control. In this work we describe a new methodology for tuning parameters of Genetic Programming algorithms using factorial designs, one-factor designs and multiple linear regression. Our experiments show that factorial designs can be used to determine which parameters have the largest effect on the algorithm's performance. This way, parameter setting efforts can focus on them, largely reducing the parameter search space. Two classical GP problems were studied, with six parameters for the first problem and seven for the second. The results show the maximum tree depth as the parameter with the largest effect on both problems. A one-factor design was performed to fine-tune tree depth on the first problem and a multiple linear regression to fine-tune tree depth and number of generations on the second.