Rapid and Thorough Exploration of Low Dimensional Phenotypic Landscapes

Abstract

This thesis presents two novel algorithms for the evolutionary optimisation of agent populations through divergent search of low dimensional phenotypic landscapes. As the eld of Evolutionary Robotics (ER) develops towards more complex domains, which often involve deception and uncertainty, the promotion of phenotypic diversity has become of increasing interest. Divergent exploration of the phenotypic feature space has been shown to avoid convergence towards local optima and to provide diverse sets of solutions to a given objective. Novelty Search (NS) and the more recent Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), are two state of the art algorithms which utilise divergent phenotypic search. In this thesis, the individual merits and weaknesses of these algorithms are built upon in order to further develop the study of divergent phenotypic search within ER. An observation that the diverse range of individuals produced through the optimisation of novelty will likely contain solutions to multiple independent objectives is utilised to develop Multiple Assessment Directed Novelty Search (MADNS). The MADNS algorithm is introduced as an extension to NS for the simultaneous optimisation of multiple independent objectives, and is shown to become more e ective than NS as the size of the state space increases. The central contribution of this thesis is the introduction of a novel algorithm for rapid and thorough divergent search of low dimensional phenotypic landscapes. The Spatial, Hierarchical, Illuminated NeuroEvolution (SHINE) algorithm di ers from previous divergent search algorithms, in that it utilises a tree structure for the maintenance and selection of potential candidates. Unlike previous approaches, SHINE iteratively focusses upon sparsely visited areas of the phenotypic landscape without the computationally expensive distance comparison required by NS; rather, the sparseness of the area within the landscape where a potential solution resides is inferred through its depth within the tree. Experimental results in a range of domains show that SHINE signi cantly outperforms NS and MAP-Elites in both performance and exploration.