A Model of Artificial Genotype and Norm of Reaction in a Robotic System
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[1] Giorgio Metta,et al. Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression. , 2013, Neural networks : the official journal of the International Neural Network Society.
[2] Andrew M. Sutton,et al. Genetic Algorithms - A Survey of Models and Methods , 2012, Handbook of Natural Computing.
[3] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[4] Massimo Pigliucci,et al. Phenotypic Plasticity: Beyond Nature and Nurture , 2001 .
[5] L. Cook. The Genetical Theory of Natural Selection — A Complete Variorum Edition , 2000, Heredity.
[6] M. Pigliucci. Genotype–phenotype mapping and the end of the ‘genes as blueprint’ metaphor , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.
[7] Martin Fodslette Meiller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .
[8] P. Alberch. From genes to phenotype: dynamical systems and evolvability , 2004, Genetica.
[9] Martin Fodslette Møller,et al. A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.
[10] Lalit M. Patnaik,et al. Genetic algorithms: a survey , 1994, Computer.
[11] Marko Pfeifer,et al. An Introduction To Genetic Analysis , 2016 .
[12] Marco Antonelli,et al. Learning the visual-oculomotor transformation: Effects on saccade control and space representation , 2015, Robotics Auton. Syst..
[13] Mitsuo Kawato,et al. Feedback-Error-Learning Neural Network for Supervised Motor Learning , 1990 .
[14] Stéphane Doncieux,et al. Beyond black-box optimization: a review of selective pressures for evolutionary robotics , 2014, Evol. Intell..