Santa Fe Trail for Artificial Ant with Analytic Programming and Three Evolutionary Algorithms

Abstract

The paper deals with a novelty tool for symbolic regression - analytic programming (AP) which is able to solve various problems from the symbolic regression domain. One of the tasks for it can be a setting an optimal trajectory for an artificial ant on Santa Fe trail which is the main application of analytic programming in this paper. In this contribution main principles of AP are described and explained. In the second part of the article how AP was used for a setting an optimal trajectory for the artificial ant according the user requirements is in detail described. An ability to create so called programs, as well as genetic programming (GP) or grammatical evolution (GE) do, is shown in that part. AP is a superstructure of evolutionary algorithms which are necessary to run AP. In this contribution 3 evolutionary algorithms were used - self organizing migrating algorithm, differential evolution and simulated annealing. The results show that the first two used algorithms were more successful than not so robust Simulated Annealing