Evolution of recurrent cascade correlation networks with distributed collaborative species

The vast research and experimental work of using EANN to evolve neural networks had achieved many successes, yet it also revealed some limitations. Aiming at boosting the EANN speed, improving its performance, the approach of Cooperative Co-evolution is introduced. Instead of one evolutionary algorithm that attempts to solve the whole problem, species representing simpler subtasks are evolved as separate instances of an evolutionary algorithm. The goal of this research is to investigate the performance of a distributed version of the collaborative coevolutionary species approach when discrete time steps are introduced into the problem, by applying the approach to the evolution of recurrent cascade correlation networks. A research tool is designed and implemented to simulate the evolution and the running of the recurrent neural network. Results are presented in which the Distributed Cooperative Coevolutionary Genetic Algorithm (DCCGA) produced higher quality solutions in fewer evolutionary cycles when compared to the standard genetic algorithm (GA). The performance of the two algorithms is analyzed and compared in two tasks: learning to recognize characters of the Morse code and learning a finite state grammar from examples.