Evolutionary Robotics (ER) is known as the methodology that uses evolutionary computation to build up controllers intended for autonomous robots. Algorithms in ER typically operate on populations of candidate controllers, initially selected through numerous distribution. This population is then repeatedly changed according to a fitness function. In the case of genetic algorithms (or “GAs”), a common method in evolutionary computation, the population of candidate controllers is repeatedly grown up according to crossover, mutation along with other GA operators and then culled according to the fitness function. This prospect controllers applied in ER applications could possibly be drawn through numerous subset of the set associated with artificial neural networks, although some applications (including SAMUEL, developed at the Naval Center for Applied Research in Artificial Intelligence) work with collections of “IF THEN ELSE” principles as being the constituent parts of an individual controller. It is theoretically possible to use any set of symbolic formulations of a control laws (sometimes called a policies in the machine learning community) as the space of possible candidate controllers. Artificial neural systems could also be used for robot learning outside of the context of evolutionary robotics. In particular, other styles relating to reinforcement learning may be used for learning robot controllers.
Developmental robotics relates to, but is different from, evolutionary robotics. ER uses populations of robots that evolve over time, whereas DevRob is interested in the way the organization of the single robot’s control system develops through experience, as time passes.
Continue reading
Evolutionary Robotics ...
Tags: evolutionary robotics, robot evolution, robot history, robot time, robotics evolution
