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Genetic Algorithms (GAs) following a parallel master-slave architecture can be effectively used to reduce searching time when fitness functions have fixed execution time. This paper presents a parallel GA architecture along with two accelerated GA operators to enhance the performance of masterslave GAs, specially when considering fitness functions with variable execution times. We explore the performance of the proposed approach, and analyse its effectiveness against the state-of-the-art. The results show a significant improvement in search times and fitness function utilisation, thus potentially enabling the use of this approach as a faster searching tool for timing-sensitive optimisation processes such as those found in dynamic real-time systems.

Hardware-Accelerated Parallel Genetic Algorithm for Fitness Functions with Variable Execution Times