Evolving neural networks that are both modular and regular: Hyperneat plus the connection cost technique

GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

Volumn , Issue , 2014, Pages 697-704

  Huizinga, Joost ^a   Mouret, Jean Baptiste ^b   Clune, Jeff ^a  

^a UNIVERSITY OF WYOMING   (United States)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Artificial neural networks; Hyper NEAT; Modularity; NSGA II; Regularity

Indexed keywords

ANIMALS; COMPLEX NETWORKS; EVOLUTIONARY ALGORITHMS; INTELLIGENT ROBOTS; SOCIAL ROBOTS;

CONNECTION-COSTS; EVOLVABILITY; EVOLVING NEURAL NETWORK; HYPER-NEAT; MODULARITY; NSGA-II; REGULARITY; STEPPING STONE;

NEURAL NETWORKS;

EID: 84905686858     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2576768.2598232     Document Type: Conference Paper

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