Population variation in genetic programming

Abstract

A new population variation approach is proposed, whereby the size of the population is systematically varied during the execution of the genetic programming process with the aim of reducing the computational effort compared with standard genetic programming (SGP). Various schemes for altering population size under this proposal are investigated using a comprehensive range of standard problems to determine whether the nature of the “population variation”, i.e. the way the population is varied during the search, has any significant impact on GP performance. The initial population size is varied in relation to the initial population size of the SGP such that the worst case computational effort is never greater than that of the SGP. It is subsequently shown that the proposed population variation schemes do have the capacity to provide solutions at a lower computational cost compared with the SGP.