Creating evolutionary algorithms by means of analytic programming - Design of new cost function

Abstract

This contribution deals with a new idea of how to create evolutionary algorithms by means of symbolic regression and Analytic Programming. The motivation was not only to tune some existing algorithms to their better performance, but also to find a new robust evolutionary algorithm. In this study operators of Differential Evolution (DE), SelfOrganizing Migrating Algortithm (SOMA), Hill Climbing were used during a process of Analytic Programming. The results showed that AP was able to find the originally defined DE, but also new structure which has a similar behaviour but slower convergence in multimodal function than DE. This, in further work, leads to including the conditions of convergence to CostFunction. Results produced in 100 repeated simulations are displayed in graphical and tabular form.