Analysis of Genetic Programming Runs

We have analysed runs of 12 different genetic programming problems. Some of the problems are the ‘toy’ problems used in generic programming research and some are significant real world applications. We have generated log files of the runs and looked for recurring and unusual patterns and whether there are any differences between the toy problems and the real world problems. The major finding is that some programs are being evaluated many times. In the real-world problems 30-78% of the time was spent on reevaluating programs that had already been evaluated. For problems where the evaluation function is expensive significant savings are possible if evaluated programs are cached. A surprising finding was that, for two of the real world problems, a very large number of the evaluations were of 1-node programs.