Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune To cite this version: Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune. Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique. GECCO ’14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, ACM, 2014, Vancouver, Canada. pp.697-704. hal-01300699 HAL Id: hal-01300699 https://hal.archives-ouvertes.fr/hal-01300699 Submitted on 11 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. To appear in: Proceedings of the Genetic and Evolutionary Computation Conference. 2014 Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga Jean-Baptiste Mouret Jeff Clune Evolving AI Lab ISIR, Université Pierre et Evolving AI Lab Department of Computer Marie Curie-Paris 6 Department of Computer Science CNRS UMR 7222 Science University of Wyoming Paris, France University of Wyoming
[email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION One of humanity’s grand scientific challenges is to create ar- An open and ambitious question in the field of evolution- tificially intelligent robots that rival natural animals in intel- ary robotics is how to produce robots that possess the intel- ligence and agility.