Neural Synthesis of Artificial Organisms through Evolution
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A methodology for evolving the control systems of autonomous robots has not yet been well established. In this paper we will show different examples of applications of evolutionary robotics to real robots by describing three different approaches to develop ...
Co-evolution (i.e. the evolution of two or more competing populations with coupled fitness) has several features that may potentially enhance the power of adaptation of artificial evolution. In particular, as discussed by Dawkins and Krebs [3], competing p ...
The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot. The evolutionary approach to the development of neural controllers for autonomous agents has been successfully used by many researchers, but most ...
Evolutionary robotics is an interesting novel approach to shape the control system of autonomous robots. This explores issues related to re-adaptation in changed environments of a population of evolved individuals. Experimental studies are reported for gen ...
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...
In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the ...