Research in robotics programming is divided in two camps. The direct hand programmming approach uses an explicit model or a behavioral model (subsumption architecture). The machine learning community uses neural network and/or genetic algorithm. We claim that hand programming and learning are complementary. The two approaches used together can be orders of magnitude more powerful than each approach taken separately. We propose a method to combine them both. It includes three concepts: syntactic constraints to restrict the search space, hand-made problem decomposition, hand given fitness. We use this method to solve a complex problem (eight-legged locomotion). It needs 5000 less evaluations compared to when genetic algorithm are used alone.
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Gruau et al. (2001) studied this question.