70,743 research outputs found
Non-photorealistic Rendering with Cartesian Genetic Programming using Graphic Processing Units
Non-photorealistic rendering (NPR) is concerned with the algorithm generation of images having unrealistic characteristics, for example, oil paintings or watercolour. Using genetic programming to evolve aesthetically pleasing NPR images is a relatively new approach in the art field, and in the majority of cases it takes a lot of time to generate results. With use of Cartesian genetic programming (CGP) and graphic processing units (GPUs), we can improve the performance of NPR image evolution. Evolutionary NPR can render images with interesting, and often unexpected, graphic effects. CGP provides a means to eliminate large, inefficient rendering expressions, while GPU acceleration parallelizes the calculations, which minimizes the time needed to get results. By using these tools, we can speed up the image generation process. Experiments revealed that CGP expressions are more concise, and search is more exploratory, than in tree-based approaches. Implementation of the system with GPUs showed significant speed-up
Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results
The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device
Exploiting Tournament Selection for Efficient Parallel Genetic Programming
Genetic Programming (GP) is a computationally intensive technique which is
naturally parallel in nature. Consequently, many attempts have been made to
improve its run-time from exploiting highly parallel hardware such as GPUs.
However, a second methodology of improving the speed of GP is through
efficiency techniques such as subtree caching. However achieving parallel
performance and efficiency is a difficult task. This paper will demonstrate an
efficiency saving for GP compatible with the harnessing of parallel CPU
hardware by exploiting tournament selection. Significant efficiency savings are
demonstrated whilst retaining the capability of a high performance parallel
implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved
with a peak rate of 96 billion GPop/s for classification type problems
Evolution of the discrete cosine transform using genetic programming
Compression of 2 dimensional data is important for the efficient transmission, storage and manipulation
of Images. The most common technique used for lossy image compression relies on fast application of
the Discrete Cosine Transform (DCT). The cosine transform has been heavily researched and many
efficient methods have been determined and successfully applied in practice; this paper presents a novel
method for evolving a DCT algorithm using genetic programming. We show that it is possible to evolve a
very close approximation to a 4 point transform. In theory, an 8 point transform could also be evolved
using the same technique
Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks.
Genetic studies are increasingly based on short noisy next generation scanners. Typically complete DNA sequences are assembled by matching short NextGen sequences against reference genomes. Despite considerable algorithmic gains since the turn of the millennium, matching both single ended and paired end strings to a reference remains computationally demanding. Further tailoring Bioinformatics tools to each new task or scanner remains highly skilled and labour intensive. With this in mind, we recently demonstrated a genetic programming based automated technique which generated a version of the state-of-the-art alignment tool Bowtie2 which was considerably faster on short sequences produced by a scanner at the Broad Institute and released as part of The Thousand Genome Project
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