Evolving Connectionist and Hybrid Systems: Methods, Tools, Applications

Abstract

Evolving Connectionist Systems (ECOS) are neural network systems that develop their structure, functionality and internal representation through continuous learning from data and interaction with the environment. ECOS can also evolve through generations of populations using evolutionary computation, but the focus of the presentation is on: (1) Adaptive learning and improvement of each individual model; (2) Knowledge representation, knowledge adaptation and knowledge extraction. The learning process can be: on-line, off-line, incremental, supervised, unsupervised, active, sleep/dream, etc.