Accelerating Genetic Programming through Graphics Processing Units.

Banzhaf, Wolfgang; Harding, Simon; Langdon, William B.; Wilson, Garnett

doi:10.1007/978-0-387-87623-8_15

Cited by 30 publications

(26 citation statements)

References 25 publications

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“…The efficiency of rules interpreters is often reported using the number of primitives interpreted by the system per second, similarly to Genetic Programming (GP) interpreters, which determine the number of GP operations per second (GPops/s) [3,[30][31][32]. GP interpreters evaluate expression trees, which represent solutions to perform a user-defined task.…”

Section: Rules Interpreter Performancementioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

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Section: Rules Interpreter Performancementioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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“…A number of evolutionary computation practitioners have demonstrated significant speed-ups in computation for some time now using a number of distributed and parallel computing techniques, due to the fact that evolutionary computationbased algorithms are easily parallelizable [1,4]. The first GPU-centered applications to use evolutionary algorithms in general naturally applied them to textures for use in image processing.…”

Section: Gpgpu Programming and Related Workmentioning

confidence: 99%

“…The adoption of graphics processing units (GPUs) for parallel computing is due to both the low price point of the GPU hardware compared to other options for parallel processing and the rate at which the computing power of the GPU hardware brought to market increases [1]. Evolutionary computation in general, including genetic programming (GP), can usually be readily adapted to parallel computing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Wilson

Banzhaf

2010

Genet Program Evolvable Mach

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We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console by using a publicly available common framework for the devices called XNA (for ''XNA's Not Acronymed''). By constructing the LGP within this framework, we effectively produce an LGP ''game'' for PC and XBox 360 that displays results as they evolve. We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described. Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the metrics of GP operations per second, actual time elapsed, This work is based on an earlier work: Deployment

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“…Whereas the numeric integration of RD PDEs is already computationally expensive, evolving a population of these PDEs is even more demanding. Luckily, both GP and RD numeric integration are well suited for parallelization on top of GPU (Graphics Processing Unit) hardware [2,16,20,24].…”

Section: Introductionmentioning

confidence: 99%

Evolving Reaction-Diffusion Systems on GPU

Yamamoto

Banzhaf

Collet

2011

Progress in Artificial Intelligence

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Abstract. Reaction-diffusion systems contribute to various morphogenetic processes, and can also be used as computation models in real and artificial chemistries. Evolving reaction-diffusion solutions automatically is interesting because it is otherwise difficult to engineer them to achieve a target pattern or to perform a desired task. However most of the existing work focuses on the optimization of parameters of a fixed reaction network. In this paper we extend this state of the art by also exploring the space of alternative reaction networks, with the help of GPU hardware. We compare parameter optimization and reaction network optimization on the evolution of reaction-diffusion solutions leading to simple spot patterns. Our results indicate that these two optimization modes tend to exhibit qualitatively different evolutionary dynamics: in the former, the fitness tends to improve continuously in gentle slopes, while the latter tends to exhibit large periods of stagnation followed by sudden jumps, a sign of punctuated equilibria.

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Accelerating Genetic Programming through Graphics Processing Units.

Cited by 30 publications

References 25 publications

Speeding up multiple instance learning classification rules on GPUs

Speeding up multiple instance learning classification rules on GPUs

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Evolving Reaction-Diffusion Systems on GPU

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