A method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs is presented. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (
). To assess the evolvability of the system we applied a modified version of the method introduced in  to measure rate of evolution. Our results show that such method effectively reveals how evolution proceeds under different parameter settings. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.
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