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Using genetic improvement & code transplants to specialise a C++ program to a problem class
- In 17th European Conference on Genetic Programming (EuroGP
, 2014
"... Abstract. Genetic Improvement (GI) is a form of Genetic Program-ming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combi-natorial Interaction Testi ..."
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Abstract. Genetic Improvement (GI) is a form of Genetic Program-ming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combi-natorial Interaction Testing (CIT), using automated code transplanta-tion. Our GI-evolved solver achieves overall 17 % improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem.
Genetically improved CUDA C++ software
- In 17th European Conference on Genetic Programming (EuroGP
, 2014
"... Abstract. Genetic Programming (GP) may dramatically increase the performance of software written by domain experts. GP and autotuning are used to optimise and refactor legacy GPGPU C code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards ..."
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Cited by 10 (9 self)
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Abstract. Genetic Programming (GP) may dramatically increase the performance of software written by domain experts. GP and autotuning are used to optimise and refactor legacy GPGPU C code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards are reported compared to the original kernel on the same hardware. 1
Improving 3D Medical Image Registration CUDA Software with Genetic Programming
"... Genetic Improvement (GI) is shown to optimise, in some cases by more than 35%, a critical component of health-care industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically op-timise the curren ..."
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Cited by 2 (2 self)
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Genetic Improvement (GI) is shown to optimise, in some cases by more than 35%, a critical component of health-care industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically op-timise the current rate limiting CUDA parallel function in the Nifty Reg open source C++ project used to align or reg-ister high resolution nuclear magnetic resonance NMRI and other diagnostic NIfTI images. Future Neurosurgery tech-niques will require hardware acceleration, such as GPGPU, to enable real time comparison of three dimensional in the-atre images with earlier patient images and reference data. With millimetre resolution brain scan measurements com-prising more than ten million voxels the modified kernel can process in excess of 3 billion active voxels per second.
Automated Design of Algorithms and Genetic Improvement : Contrast and Commonalities
- In Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion, GECCO Comp ’14
, 2014
"... Automated Design of Algorithms (ADA) and Genetic Im-provement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolution-ary search methods and successfully produce human com-petitive programs. ADA ..."
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Automated Design of Algorithms (ADA) and Genetic Im-provement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolution-ary search methods and successfully produce human com-petitive programs. ADA and GI are used for improving functional properties such as quality of solution and non-functional properties, e.g. speed, memory and, energy con-sumption. Only GI of the two has been used to fix bugs, probably because it is applied globally on the whole source code while ADA typically replaces a function or a method locally. While GI is applied directly to the source code ADA works ex-situ, i.e. as a separate process from the program it is improving. Although the methodologies overlap in many ways they differ on some fundamentals and for further progress to be made researchers from both disciplines should be aware of each other’s work.
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"... This paper 1 presents a brief outline of an approach to online genetic improvement. We argue that existing progress in genetic improvement can be exploited to support adaptivity. We illustrate our proposed approach with a ‘dreaming smart device ’ example that combines online and offline machine lear ..."
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This paper 1 presents a brief outline of an approach to online genetic improvement. We argue that existing progress in genetic improvement can be exploited to support adaptivity. We illustrate our proposed approach with a ‘dreaming smart device ’ example that combines online and offline machine learning and optimisation. Categories and Subject Descriptors
