B. Burlacu, M. Affenzeller, M. Kommenda, S. M. Winkler, G. K. Kronberger - Evolution Tracking in Genetic Programming - Proceedings of the 24th European Modeling and Simulation Symposium EMSS 2012, Vienna, Austria, 2012
Much effort has been put into understanding the
artificial evolutionary dynamics within genetic
programming (GP). However, the details are yet unclear so far, as to which elements make GP so powerful. This paper presents an attempt to study the evolution of a population of computer programs using HeuristicLab. A newly developed methodology for recording heredity information, based on a general conceptual framework of evolution, is employed for the analysis of algorithm behavior on a symbolic regression benchmark problem.
In our example, we find the complex interplay between
selection and crossover to be the cause for size increase in the population, as the average amount of genetic information transmitted from parents to offspring remains constant and independent of run constraints (i.e., tree size and depth limits). Empirical results reveal many interesting details and confirm the validity and generality of our approach, as a tool for understanding the complex aspects of GP.