Refinement and On-Line Adaptation of Neurocontrollers Through Particle swarming
Although neuroevolution is powerful in discovering competent neurocontrollers, it is difficult to achieve (1) high accuracy, and (2) on-line adaptation to changes in the environment. In this project, local adaptation using Particle Swarming is shown to solve both problems. A competent neurocontroller is first evolved, and a population consisting of slight modifications to it is then formed. This population is further adapted as a swarm, allowing fine tuning and on-line response to changes in the environment.