Rapid and Thorough Exploration of Low Dimensional Phenotypic Landscapes
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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.
Authors
Smith, DavidCollections
- Theses [4222]