Gene Regulatory Network Evolution Through Augmenting Topologies

Artificial gene regulatory networks are biologicallyinspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm or an evolution strategy in order to optimize the network for a specific task. However, the empirical performance of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric which allows genetic similarity to be used for speciation and variation of gene regulatory networks. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm’s use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon gene regulatory network structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard genetic algorithm and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments.