Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Advanced FPGA Design: Architecture, Implementation, and Optimization
Advanced FPGA Design: Architecture, Implementation, and Optimization
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Design & Implementation of Real-time Parallel GA Operators on the IBM Cell Processor
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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We present two different single-core parallel SIMD linear genetic programming (LGP) systems for the IBM Cell Processor on the Playstation3. Our algorithms harness their computational power from the parallel capabilities of the Cell Processor. We implement two evolutionary algorithms and look at the classical problem of symbolic regression of functions. The first LGP generates a single offspring and selection from the population occurs randomly. The second algorithm generates two offspring and selection from the population is performed using k-tournament with k = 2. Mutation occurs at macro and micro levels. Both SIMD instructions and register operands are subject to mutation. We use a static population of 648 individuals due to memory and data transfer restrictions and, experiments are constrained to 300 seconds of computational time. Our results indicate that both EAs perform equally well though the first algorithm is faster and outperforms the 2nd algorithm in some cases. We speculate that the speed at which generations are iterated through is significantly greater than that of a typical tree-based GP and sequential linear GP.