Davy Smith, Laurissa Tokarchuk, and Chrisantha Fernando Abstract Novelty search is an algorithm which proposes open-ended exploration of the search space by maximising behavioural novelty, removing the need for an objective fitness function. However, we show that when applied to complex tasks, training through novelty alone is not sufficient to produce useful controllers. Alongside this, the definition of phenotypic behaviour … Read More

Davy Smith, Laurissa Tokarchuk and Geraint Wiggins Abstract Multiple assessment directed novelty search (MADNS), introduced by the authors in [20], is an extension to the novelty search algorithm which exploits the observation that populations optimised for phenotypic novelty may contain solutions to multiple independent and conflicting objectives. It has been shown that, through the application of MADNS,an evolutionary trajectory may … Read More

Davy Smith, Laurissa Tokarchuk and Geraint Wiggins Abstract Novelty search is an evolutionary approach which promotes phenotypic diversity in a population. Novelty search has been successfully applied to a wide range of domains and a number of variants have been proposed. Here we introduce Multiple Assessment Directed Novelty Search (MADNS), which exploits the notion that a diverse population optimised through … Read More

Davy Smith, Laurissa Tokarchuk and Geraint Wiggins Abstract Exploration of the search space through the optimisation of phenotypic diversity is of increasing interest within the field of evolutionary robotics. Novelty search and the more recent MAP-Elites are two state of the art evolutionary algorithms which diversify low dimensional phenotypic traits for divergent exploration. In this paper we introduce a novel … Read More