Expediting GA-Based Evolution Using Group Testing Techniques for Reconfigurable Hardware

Autonomous repair and refurbishment of reprogrammable logic devices using genetic algorithms can improve the fault tolerance of remote mission-critical systems. The goal of increasing availability by minimizing the repair time is addressed in this paper using a CGT-pruned genetic algorithm. The proposed method utilizes resource performance information obtained using combinatorial group testing (CGT) techniques to evolve refurbished configurations in fewer generations than conventional genetic algorithms. A 3-bit times 2-bit multiplier circuit was evolved using both conventional and CGT-pruned genetic algorithms. Results show that the new approach yields completely refurbished configurations 37.6% faster than conventional genetic algorithms. In addition it is demonstrated that for the same circuit, refurbishment of partially-functional configurations is a more tractable problem than designing the configurations when using genetic algorithms as results show the former to take 80% fewer generations

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