Evolutionary robotics: the Sussex approach

By Inman R. Harvey, P. Husbands, D. Cliff, A. Thompson, N. Jakobi. 1997

We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots, simulated robots, co-evolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Sub-fields of evolutionary computation such as evolutionary robotics (Harvey et al., 1997; Nolfi and Floreano, 2000) and neuroevolution (Yao, 1999) specifically research algorithms that, exploiting”
Fitness functions in evolutionary robotics: A survey and analysis (2009, unknown): “The field of ER has been reviewed in various publications [1–8].”
Horizons for the Enactive Mind: Values, Social Interaction, and Play (2010, background): “Enactivism thus differs from other nonrepresentational views such as Gibsonian ecological psychology on this point ( Varela, Thompson, and Rosch 1991 , 203 – 204).”
Evolutionary Robotics: A New Scientific Tool for Studying Cognition (2005, methodology): “sometimes also to their bodily and sensorimotor design [1, 2].”
Cooperative Multi-Agent Learning: The State of the Art (2005, unknown): “Other investigations concerned games such as Tic-TacToe [5], Backgammon [175, 174], Mancala [56], Othello [211], pursuit-evasion [48, 88], Go [131], Chess [234, 121], Poker [122], Blackjack [229] and Tag [187].”