Program evolution with explicit learning

In genetic programming (GP) and most other evolutionary computing approaches, the knowledge learned during the evolutionary processing is implicitly encoded in the population. A small family of approaches, known as estimation of distribution algorithms, learn this knowledge directly in the form of probability distributions. In this research, we proposed a new approach for program synthesis - program evolution with explicit learning (PEEL), belonging to this family. PEEL learns probability distributions from previous generations and stochastically generates new populations according to this distribution. PEEL is intrinsically different from GP systems because it abandons conventional GP genetic operators and does not maintain population. On the benchmark problems we have studied, this approach shows at least comparable performance to GP.