Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution

If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more complex problems? We begin to investigate this question by introducing Tweaking Mutation Behaviour Learning (TMBL), a form of evolutionary computation designed to meet this challenge. Whereas Genetic Programming (GP) typically involves creating a large pool of initial solutions and then shuffling them (with crossover and mutation) over relatively few generations, TMBL focuses on the cumulative acquisition of small adaptive mutations over many generations. In particular, we aim to reduce limits on long term fitness growth by encouraging tweaks: changes which affect behaviour without ruining the existing functionality. We use this notion to construct a standard representation for TMBL. We then experimentally compare TMBL against linear GP and tree-based GP and find that TMBL shows strong signs of being more conducive to the long term growth of fitness.