Evolving Petri Nets for Situation Recognition.
Anders Dahlbom, Lars Niklasson, Göran Falkman · 2010
Situation recognition is an important problem to address for developing newcapabilities in the surveillance domain. It is concerned with recognizing a priori defined situations of interest, which can be of concurrent and temporal nature, possibly occurring in a continuous flow of data and information. It is however a complex task to manually define what constitutes an interesting situation, and we therefore investigate the possibility of using genetic algorithms for evolving Petri nets for situation recognition. Our results show that: (1) it is possible to evolve complex Petri nets, (2) it is possible to increase the performance of manually designed Petri nets, and (3) a dynamic genome representation consisting of complex genes is beneficial compared to a representation consisting of bit strings.