Abstract: Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Neuromodulation has been used to facilitate unsupervised learning by adapting neural network weights. Multiobjective evo-lution of neural network topology and weights has been used to design neurocontrollers for autonomous robots. This paper presents a novel multiobjective evolutionary neurocontroller with unsupervised learning for robot navigation. Multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with neuromodulated learning. NEAT-MODS is an NSGA-II based multiobjective neurocontroller that uses two conflicting objectives. The first rewards the robot when it moves in a direct manner with minimal turning; the second objective is to reach as many targets as possible. NEAT-MODS uses speciation, a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. The effectiveness of the design is demonstrated using a series of experiments with a simulated robot traversing a simple maze containing target goals. It is shown that when neuromodulated learning is combined with multiobjective evolution, better-performing neural controllers are synthesized than by evolution alone. Secondly, it is demonstrated that speciation is unnecessary in neuro-modulated neuroevolution, as neuromodulation preserves topological innovation. The proposed neuromodulated approach is found to be statistically superior to NEAT-MODS alone when applied to solve a multiobjective navigation problem. PDF
Keywords: Artificial neural network · Hebbian learning · Multiobjective · NEAT-MODS · Neuromodulation · Speciation