The Evolutionary Robotics (ER) is an area of autonomous robotics, in which drivers are developed through evolution robots using genetic algorithms.
It usually evolves Neural Networks as it follows the reference biological and simplifies the design and representation in genetic algorithm.
The foundation of the RE is relationship to work at the CNR in Rome in the ’90s, but the initial idea of encoding the control system of a robot in a genome and evolutionary data of the 80 late.
Cliff, Harvey and Husbands of COGS at Sussex University introduced the term evolutionary robotics in the year 1993. In 1992 and 1993, two teams, Floreano and Mondada at EPFL da Lausanne and COGS group reported the first experiments of artificial evolution of autonomous robots.
The initial success of this fledgling business launched a major research attempting to define the potential of this approach to the problem.
Lately, the difficulty of growing complexity of tasks, as in the symbolic approach, it has directed attention to the theoretical side of the discipline by abandoning the view of engineering.
The Evolutionary Robotics has several objectives, often simultaneously. The point of view of engineering creates robot controllers to perform useful tasks in the real world. The biology and other life sciences obtained from simulations that reproduce physiological to ecological phenomena. The philosophy of science can analyze systems with epistemic value.