Multi objective symbolic regression

Abstract

Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite athletes more information and better expressions can be generated than by using single, or even double objective functions. Symbolic regression, multi-objective, non dominated sorting, genetic programming.