Detecting and assignment of unexpected tasks in SoC design process using genetic programming

During SoC design designers can meet many problems. One of it is Concurrent real-time optimization. It is a very important problem in operation research. The problem appears when exist many potentials solutions of one optimization issue. Each solution demands different parameters to optimize. Therefore the process can be split into two phases. Each phase impacts another in real-time. Such a problem can appear in detecting and assignment of unexpected tasks in SoC design process. In the paper we propose a genetic programming solution to detect the optimal way to solve unexpected situations which appear during the work of the system. Proposed methodology starts from randomly constructed genotypes. During the evolution process the algorithm generates new individuals using standard genetic operators: mutation, crossover, cloning and rank selection.