Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Advanced FPGA Design: Architecture, Implementation, and Optimization
Advanced FPGA Design: Architecture, Implementation, and Optimization
Design & Implementation of Parallel Linear GP for the IBM Cell Processor
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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We present a set of single-core designed parallel SIMD Genetic Algorithm (GA) operators aimed at increasing computational speed of genetic algorithms. We use a discrete-valued chromosome representation. The explored operators include: single gene mutation, uniform crossover and a fitness evaluation function. We discuss their low-level hardware implementations on the Cell Processor. We use the Knapsack problem as a proof of concept, demonstrating performances of our operators. We measure the scalability in terms of generations per second. Using the architecture of the Cell Processor and a static population size of 648 individuals, we achieved 11.6 million generations per second on one Synergetic Processing Element (SPE) core for a problem size n = 8 and 9.5 million generations per second for a problem size n = 16. Generality for a problem size n multiple of 8 is also shown. Executing six independent concurrent GA runs, one per SPE core, allows for a rough overall estimate of 70 million generations per second and 57 million generations per second for problem sizes of n = 8 and n = 16 respectively.