Evolutionary construction and adaptation of intelligent systems

This paper introduces evolutionary techniques for automatically constructing intelligent self-adapting systems, capable of modifying their inner structure in order to learn from experience and self-adapt to a changing environment. These evolutionary techniques comprise an evolutionary system that is engineered by grammar-guided genetic programming, enabling the development of sub-symbolic and symbolic intelligent systems: artificial neural networks and knowledge-based systems, respectively. A context-free-grammar based codification system for artificial neural networks and rules, an initialization method and a crossover operator have been designed to properly balance the exploration and exploitation capabilities of the proposed system. This speeds up the convergence process and avoids trapping in local optima. This system has been applied to a medical domain: the detection of knee injuries from the analysis of isokinetic time series. The results of the evolved symbolic and sub-symbolic intelligent systems have been statistically compared with each other as part of a quantitative and qualitative performance analysis.