Context-aware evolvable system framework for environment identifying systems

This paper proposes a novel framework for adaptive and intelligent systems that can be used under dynamic and uneven environments by taking advantage of environment context identification. Adaptation to dynamically changing environments is very important since advanced applications become pervasive and ubiquitous. The proposed framework, callesd CAES (Context-Aware Evolvable System), adopts the concept of context-aware and the evolutionary computing, and the system working environments are learned (clustered) and identified as environmental contexts. The context-awareness has been carried out by unsupervised learning, Fuzzy ART. Genetic algorithm (GA) is used to explore the most effective action configuration for each identified context. The knowledge of the individual context and its associated chromosomes representing optimal action configurations is accumulated and stored in the context knowledge base. Once the context knowledge is constructed, the system can adapt to varying environment in real-time. The framework of CAES has been tested in the area of intelligent vision application where most approaches show vulnerability under dynamically changing environments. The superiority of the proposed scheme is shown using three face image data sets: Inha, FERET, and Yale.