The Experts below are selected from a list of 104271 Experts worldwide ranked by ideXlab platform
G. Kalloo - One of the best experts on this subject based on the ideXlab platform.
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Genetic Improvement of Tomato - Genetic Improvement of tomato.
Monographs on Theoretical and Applied Genetics, 1991Co-Authors: G. KallooAbstract:Tomato is a crop grown worldwide which is being increasingly studied particularly from the aspects covered here, i.e. cytology, Genetics, breeding and biotechnology. This text examines the Genetic Improvement of tomatoes. Among the topics covered are: reproductive biology and its implications in tomato Improvement; mitosis and meiosis studies; linkage and mutagenesis; classical and molecular Genetics; isozymes and its applications in breeding practices; collection, exploration, evaluation, utilization and conservation of germplasm; Improvement through pedigree-based selection, population Improvement and multitrait selection; various aspects of interspecific and intergeneric hybridization.
Alberto Tonda - One of the best experts on this subject based on the ideXlab platform.
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A general-purpose framework for Genetic Improvement
2016Co-Authors: Francesco Marino, Giovanni Squillero, Alberto TondaAbstract:Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behavior; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software’s intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering’s idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.
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PPSN - A General-Purpose Framework for Genetic Improvement
Parallel Problem Solving from Nature – PPSN XIV, 2016Co-Authors: Francesco Marino, Giovanni Squillero, Alberto TondaAbstract:Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behavior; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software’s intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering’s idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.
Yue Jiang - One of the best experts on this subject based on the ideXlab platform.
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Genetic Improvement of Grain Quality Promoted by High and New Technology in Rice
The Journal of Agricultural Science, 2018Co-Authors: Bo Peng, Dong-yan Kong, Lu-lu He, Meng-ge Li, Tondi-yacouba Nassirou, Xiao-hua Song, Juan Peng, Yu Peng, Jun Li, Yue JiangAbstract:Rice (Oryza sativa L.) is considered as one of the most important food crops all over the world. Because of the Improvement of the actual living standards, there is more and more demand for improved grain quality. Although, the grain quality is a very complex quantitative trait in rice, it has great scientific significance and practical application value for the Improvement of the quality of rice grain using high and up to date techniques. Here, we focus on the new advances in the application of the Genetic Improvement of grain quality by high throughput sequencing, genomic editing, near infrared spectroscopy, scanning electron microscopy, and other new techniques. Simultaneously, the application prospects of these high and up to date techniques have also been expected in the Genetic Improvement of rice quality. Thus, this will provide important information for Genetic Improvement of grain quality in rice breeding.
Justyna Petke - One of the best experts on this subject based on the ideXlab platform.
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Genetic Improvement of Software (Dagstuhl Seminar 18052)
2018Co-Authors: Justyna Petke, Claire Le Goues, Stephanie Forrest, W B LangdonAbstract:We document the program and the immediate outcomes of Dagstuhl Seminar 18052 "Genetic Improvement of Software". The seminar brought together researchers in Genetic Improvement (GI) and related areas of software engineering to investigate what is achievable with current technology and the current impediments to progress and how GI can affect the software development process. Several talks covered the state-of-the-art and work in progress. Seven emergent topics have been identified ranging from the nature of the GI search space through benchmarking and practical applications. The seminar has already resulted in multiple research paper publications. Four by participants of the seminar will be presented at the GI workshop co-located with the top conference in software engineering - ICSE. Several researchers started new collaborations, results of which we hope to see in the near future.
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Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation
IEEE Transactions on Software Engineering, 2018Co-Authors: Justyna Petke, Mark Harman, W B Langdon, Westley WeimerAbstract:Genetic Improvement uses automated search to find improved versions of existing software. Genetic Improvement has previously been concerned with improving a system with respect to all possible usage scenarios. In this paper, we show how Genetic Improvement can also be used to achieve specialisation to a specific set of usage scenarios. We use Genetic Improvement to evolve faster versions of a C++ program, a Boolean satisfiability solver called MiniSAT, specialising it for three different applications, each with their own characteristics. Our specialised solvers achieve between 4 and 36 percent execution time Improvement, which is commensurate with efficiency gains achievable using human expert optimisation for the general solver. We also use Genetic Improvement to evolve faster versions of an image processing tool called ImageMagick, utilising code from GraphicsMagick, another image processing tool which was forked from it. We specialise the format conversion functionality to greyscale images and colour images only. Our specialised versions achieve up to 3 percent execution time Improvement.
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GECCO (Companion) - New operators for non-functional Genetic Improvement
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017Co-Authors: Justyna PetkeAbstract:Genetic Improvement uses automated search to find improved versions of existing software. Typically software is modified using either delete, copy or replace operations at the level of source code, its abstract syntax tree, binary or assembly representaion. Impressive Improvements have been achieved through this approach, yet research in the use of other search operators is largely unexplored. We propose several ways for devising new search operators for Improvement of non-functional properties using a Genetic Improvement apporach.
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Genetic Improvement of gpu software
Genetic Programming and Evolvable Machines, 2017Co-Authors: W B Langdon, Justyna Petke, Brian Y H Lam, Marc Modat, Mark HarmanAbstract:We survey Genetic Improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that Genetic programming can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial Improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with Genetic programming in combination with human input.
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GECCO (Companion) - Genetic Improvement for Code Obfuscation
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016Co-Authors: Justyna PetkeAbstract:Genetic Improvement (GI) is a relatively new area of software engineering and thus the extent of its applicability is yet to be explored. Although a growing interest in GI in recent years started with the work on automatic bug fixing, the area flourished when results on optimisation of non-functional software properties, such as efficiency and energy consumption, were published. Further success of GI in transplanting functionality from one program to another leads to a question: what other software engineering areas can benefit from the use of Genetic Improvement techniques? We propose to utilise GI for code obfuscation.
Francesco Marino - One of the best experts on this subject based on the ideXlab platform.
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A general-purpose framework for Genetic Improvement
2016Co-Authors: Francesco Marino, Giovanni Squillero, Alberto TondaAbstract:Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behavior; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software’s intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering’s idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.
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PPSN - A General-Purpose Framework for Genetic Improvement
Parallel Problem Solving from Nature – PPSN XIV, 2016Co-Authors: Francesco Marino, Giovanni Squillero, Alberto TondaAbstract:Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behavior; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software’s intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering’s idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.