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Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
William B. Langdon,Koza John R +1 more
- 01 May 1998
- Iss: 1
221
TL;DR: This book should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence, and to practitioners working in all of these areas and to those interested in automatic programming.
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Abstract: From the Publisher:
Computers that "program themselves" has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. In a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This is the main focus Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! addresses. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.
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Citations
Statistical strategies for avoiding false discoveries in metabolomics and related experiments
David Broadhurst,Douglas B. Kell +1 more
TL;DR: A list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact is provided, and a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers are suggested.
High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
Jess Allen,Hazel M. Davey,David Broadhurst,Jim K. Heald,Jeremy John Rowland,Stephen G. Oliver,Douglas B. Kell +6 more
TL;DR: It is shown that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect by using appropriate clustering and machine learning techniques, the latter based on genetic programming.
Genetic algorithm: review and application
TL;DR: The integration of genetic algorithm with object oriented programming approaches is described and the very high level languages like Python or Perl are more productive in list processing or string processing than C/C++/Java.
523
Metabolic footprinting and systems biology: the medium is the message
TL;DR: The principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy to study the inner structure and behaviour of a system are reviewed.
Proposed minimum reporting standards for data analysis in metabolomics
Royston Goodacre,David Broadhurst,Age K. Smilde,Bruce S. Kristal,J. David Baker,Richard D. Beger,Conrad Bessant,Susan C. Connor,Giorgio Capuani,Andrew Craig,Timothy M. D. Ebbels,Douglas B. Kell,Cesare Manetti,Jack Newton,Giovanni Paternostro,Ray Somorjai,Michael Sjöström,Johan Trygg,Florian Wulfert +18 more
TL;DR: The goal of this group is to define the reporting requirements associated with the statistical analysis of metabolite data with respect to other measured/collected experimental data (often called meta-data).
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