Lamarckian Inheritance in Neuromodulated Multiobjective Evolutionary Neurocontrollers

TLDR: It is shown that when Lamarckian inheritance is combined with evolved neurmodulated learning, neural controllers are synthesized in fewer generations than by neuromodulated evolution alone.

Abstract: This paper presents a novel evolutionary multiobjective neurocontroller with unsupervised learning and Lamarckian inheritance for robot navigation. Multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with Lamarckian inherited neuromodulated learning. NEAT-MODS is an NSGA-II augmented multiobjective neurocon-troller that uses two conflicting objectives. NEAT-MODS uses a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. 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 Lamarckian inheritance is combined with evolved neuromodulated learning, neural controllers are synthesized in fewer generations than by neuromodulated evolution alone. The proposed Lamarckian neuromodulated approach is found to be statistically superior to neuromodulation alone when applied to solve a multiobjective navigation problem.