Improving Distributed Neuroevolution Using Island Extinction and Repopulation.

Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is the use of islands, which are also popular in improving the performance of distributed evolutionary algorithms. However, islands may experience stagnation, which prevents their convergence towards better solutions and can result in wasted computation. This work evaluates utilizing an island extinction and repopulation mechanism to avoid premature convergence using Evolutionary eXploration of Augmenting Memory Models (EXAMM), an asynchronous island based neuroevolution algorithm that progressively evolves recurrent neural networks (RNNs). In island extinction and repopulation, all members of the worst performing island are erased periodically and repopulated with mutated versions of the global best RNN. This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy. Experiments were performed using two different real-world time series datasets (coal-fired power plant and aviation flight data). With statistical significance, results show that in addition to being more scalable, this island extinction and repopulation strategy evolves better global best genomes than both EXAMM's original island based strategy and NEAT's speciation strategy. The extinction and repopulation strategy is easy to implement, and can be generically applied to other neuroevolution algorithms.