We value your privacy
In tree-based Genetic Programming, subtrees which represent potentially useful sub-solutions can be encapsulated in order to protect them and aid their prolifer-ation throughout the population. This paper investigates implementing this as a multi-run method. A two-stage encapsulation scheme based on subtree survival and frequency is compared against Automatically Defined Functions in fixed and evolved architectures and standard Genetic Programming for solving a Parity problem.
In tree-based genetic programming (GP), the most frequent subtrees on later generations are likely to constitute useful partial
solutions. This paper investigates the effect of encapsulating such subtrees by representing them as atoms in the terminal
set, so that the subtree evaluations can be exploited as terminal data. The encapsulation scheme is compared against a second
scheme which depends on random subtree selection. Empirical results show that both schemes improve upon standard GP.