The evolution of evolvability in genetic programming
Advances in genetic programming
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Neutrality and variability: two sides of evolvability in linear genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Probabilistic developmental program evolution
Proceedings of the 2010 ACM Symposium on Applied Computing
Toward an estimation of distribution algorithm for the evolution of artificial neural networks
Proceedings of the Third C* Conference on Computer Science and Software Engineering
SMCGP2: self modifying cartesian genetic programming in two dimensions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Diversity loss in general estimation of distribution algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Computational Intelligence Magazine
| Hi-index | 0.00 |
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 (ASrank). To assess the evolvability of the system we applied a modified version of the method introduced in [9] 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.