Backward-chaining Genetic Programming

Tournament selection is the most frequently used form of selection in genetic programming (GP). Tournament selection chooses individuals uniformly at random from the population. As noted in [7], even if this process is repeated many times in each generation, there is always a nonzero probability that some of the individuals in the population will not be involved in any tournament. In certain conditions, typical in GP, the number of individuals in this category can be large. Because these individuals have no influence on future generations, it is possible to avoid creating and evaluating them without altering in any significant way the course of a run. [7] proposed an algorithm, the backward chaining EA (BC-EA), to realised this, but provided limited empirical evidence of the actual savings and the experiments were restricted to fixed-length genetic algorithms. In contrast we provide a generational genetic programming implementation of BC-EA and empirically investigate the e#ciency in terms of fitness evaluations and memory use and e#ectiveness in terms of ability to solve problems of BC-GP. Results indicate that large savings can be obtained with this approach