Pareto multi-objective evolution of legged embodied organisms

The automatic synthesis of embodied creatures through artificial evolution has becomea key area of research in robotics, artificial life and the cognitive sciences.However, the research has mainly focused on genetic encodings and fitness functions.Considerably less has been said about the role of controllers and how theyaffect the evolution of morphologies and behaviors in artificial creatures. Furthermore,the evolutionary algorithms used to evolve the controllers and morphologiesare pre-dominantly based on a single objective or a weighted combination of multipleobjectives, and a large majority of the behaviors evolved are for wheeled orabstract artifacts.In this thesis, we present a systematic study of evolving artificial neural network(ANN) controllers for the legged locomotion of embodied organisms. A virtual butphysically accurate world is used to simulate the evolution of locomotion behaviorin a quadruped creature. An algorithm using a self-adaptive Pareto multi-objectiveevolutionary optimization approach is developed.The experiments are designed to address five research aims investigating: (1) thesearch space characteristics associated with four classes of ANNs with different connectivitytypes, (2) the effect of selection pressure from a self-adaptive Pareto approachon the nature of the locomotion behavior and capacity (VC-dimension) ofthe ANN controller generated, (3) the effciency of the proposed approach againstmore conventional methods of evolutionary optimization in terms of computationalcost and quality of solutions, (4) a multi-objective approach towards the comparisonof evolved creature complexities, (5) the impact of relaxing certain morphologicalconstraints on evolving locomotion controllers.The results showed that: (1) the search space is highly heterogeneous with bothrugged and smooth landscape regions, (2) pure reactive controllers not requiringany hidden layer transformations were able to produce sufficiently good legged locomotion,(3) the proposed approach yielded competitive locomotion controllers whilerequiring significantly less computational cost, (4) multi-objectivity provided a practicaland mathematically-founded methodology for comparing the complexities ofevolved creatures, (5) co-evolution of morphology and mind produced significantlydifferent creature designs that were able to generate similarly good locomotion behaviors.These findings attest that a Pareto multi-objective paradigm can spawnhighly beneficial robotics and virtual reality applications