Hugh Cartwright
Genetic Algorithms
Unfortunately, it's not possible to solve every scientific
problem in closed form. Often, the best we can do is to first
find an approximate solution, and then do some extra work to
refine it until it is of suitable quality.
If there's only one reasonable solution, it seems obvious that the way to proceed is to take our approximate solution, and keep working on it until it is of sufficient quality. Perhaps surprisingly though, a better approach may be to take a fairly large set (typically, 50 - 500) of different approximate solutions and work on improving the whole set simultaneously, even though this sounds like much more work. This is the route taken by evolutionary algorithms.
It sounds as though evolutionary algorithms might have something to do with natural evolution, and that's correct. Just as a natural population evolves through the interaction and gradual modification of many individuals, with the effect of gradually improving the fitness of all members of the population, so in an evolutionary algorithm many solutions are processed together in a combined effort to locate an optimum solution.
Some Representative Books:
§§ Hugh M. Cartwright (ed.) Artificial Neural Networks, (3rd edn.) A forthcoming text in Springer's "Methods in Molecular Biology" series. Publication early 2020.
§§ Hugh M. Cartwright (ed.) Artificial Neural Networks, (2nd edn.) Vol 1260 in the Methods in Molecular Biology series. Springer, New York. (2015). ISBN 978-1-4939-22383-3.
§§ Hugh M. Cartwright, Using Artificial Intelligence in Chemistry and Biology: A Practical Guide, Taylor & Francis, (2008).
§§ Hugh M. Cartwright & Les M. Sztandera (eds), Soft Computing Approaches in Chemistry, Springer-Verlag, Berlin, (2003).
§§ Hugh M. Cartwright (ed.), Intelligent Data Analysis in Science, Volume 4 in the Oxford Chemistry Masters Series, Oxford University Press, (2000). ISBN 0-19-850233-8.
§§ Hugh M. Cartwright, Applications of Artificial Intelligence in Chemistry, Volume 11 in the Oxford Chemistry Primers Series, Japanese translation, Maruzen Co, Tokyo, (1995). ISBN 4-621-04088-X
§§ Hugh M. Cartwright, Applications of Artificial Intelligence in Chemistry, Volume 11 in the Oxford Chemistry Primers Series, Oxford University Press, (1993). ISBN 0-19-855736-1.
Some Representative Papers:
§§ Hugh M. Cartwright, Teodora Rusu & Mariana Pinteala, Artificial Intelligence methods: Challenge in computer based polymer design, in G Maroulis, TE Simos (eds.), Computational methods in science and engineering, Amer. Inst. of Physics, (2009), 214-21
§§ Hugh M. Cartwright, Development and uses of Artificial Intelligence in chemistry, in Reviews in Computational Chemistry, Kenny Lipkowitz (ed.), Wiley-VCH, (2007), 349-390.
§§ Hugh M. Cartwright, Les Sztandera & Rohan M. Gunatillake, Genetic algorithms and neural networks in the molecular design of novel fibres, 2nd DEXA workshop: Philosophies and Methodologies for knowledge discovery, deployment and development of decision support systems, Krakow, Poland, (2006).
§§ Hugh M. Cartwright, Les Sztandera & C-C Chu, Genetic Algorithms in Molecular Design of Novel Fibers, NTC Annual Report, (2005).
§§ Les Sztandera, C-C Chu & Hugh M. Cartwright, Genetic Algorithms in Molecular Design of Novel Fibers, NTC Research Briefs, (2005).
§§ Hugh M. Cartwright, Introduction to Evolutionary Computation and Evolutionary Algorithms, in Application of Evolutionary Computation to Chemistry, Roy Johnston (ed.), Springer-Verlag, Heidelberg, (2004).
§§ Hugh M. Cartwright & David Issott, Genetic Algorithm Evolution of Fuzzy Production Rules for the On-line Control of Phenol-Formaldehyde Resin Plants, in Soft Computing Approaches in Chemistry, Hugh M. Cartwright, Les M. Sztandera (eds), Springer-Verlag, Berlin, (2003), 237-26
§§ Hugh M. Cartwright, Undergraduate Projects in the Application of Artificial Intelligence to Chemistry. I. Background, Chemical Educator, 4, (1999), 3-6.
§§ Hugh M. Cartwright, Ben Jesson & Les M. Sztandera, Intelligent algorithmic interpretation of pollutant discharges from multi-unit industrial complexes. Int. J. Intelligent Systems, 12, (1997), 655-672.
§§ Hugh M. Cartwright & Ben Jesson, The analysis of waste flow data from multi-unit industrial complexes using genetic algorithms, Modern Heuristic Search Methods, VJ Rayward-Smith (ed.), John Wiley & Sons Ltd, (1996), 183-197.
§§ Hugh M. Cartwright & Julie A. Hopkins, Evolutionary design of synthetic routes in chemistry. pp 34-48, Proceedings of the AISB Conference on Evolutionary Computing, University of Sussex, Brighton, UK, (1996), 34-48.
§§ Hugh M. Cartwright, Ben Jesson & Les M. Sztandera, Intelligent algorithmic interpretation of waste flow data from multi-unit industrial complexes, Proceedings of the 2nd Conference on Information Sciences, Wrightsville Beach, NC, (1995), 324-327.
§§ Hugh M. Cartwright, The genetic algorithm in science, Pesticide Science, 45, (1995), 171-178.
§§ Hugh M. Cartwright, Getting the timing right - the use of genetic algorithms in scheduling, in Applications of Modern Heuristic Techniques, VJ Rayward-Smith, (ed.), Alfred Waller (1995), 145-156.
§§ Hugh M. Cartwright & Jamie R. Cattell, Studies of continuous-flow chemical synthesis using genetic algorithms, in Applications of Modern Heuristic Techniques, VJ Rayward-Smith, (ed.), Alfred Waller, (1995), 129-144.
§§ Hugh M. Cartwright & Andrew Tuson, Genetic Algorithms and flowshop scheduling: towards the development of a real-time process control system. Lecture Notes in Computer Science, (1994), 277-290.
§§ Hugh M. Cartwright & Robert A. Long, Simultaneous optimization of chemical flowshop sequencing and topology using Genetic Algorithms, Ind. Eng. Chem. Res., 32, (1993), 2706-2713.
DOI: 10.1021/ie00023a037
If there's only one reasonable solution, it seems obvious that the way to proceed is to take our approximate solution, and keep working on it until it is of sufficient quality. Perhaps surprisingly though, a better approach may be to take a fairly large set (typically, 50 - 500) of different approximate solutions and work on improving the whole set simultaneously, even though this sounds like much more work. This is the route taken by evolutionary algorithms.
It sounds as though evolutionary algorithms might have something to do with natural evolution, and that's correct. Just as a natural population evolves through the interaction and gradual modification of many individuals, with the effect of gradually improving the fitness of all members of the population, so in an evolutionary algorithm many solutions are processed together in a combined effort to locate an optimum solution.
Some Representative Books:
§§ Hugh M. Cartwright (ed.) Artificial Neural Networks, (3rd edn.) A forthcoming text in Springer's "Methods in Molecular Biology" series. Publication early 2020.
§§ Hugh M. Cartwright (ed.) Artificial Neural Networks, (2nd edn.) Vol 1260 in the Methods in Molecular Biology series. Springer, New York. (2015). ISBN 978-1-4939-22383-3.
§§ Hugh M. Cartwright, Using Artificial Intelligence in Chemistry and Biology: A Practical Guide, Taylor & Francis, (2008).
§§ Hugh M. Cartwright & Les M. Sztandera (eds), Soft Computing Approaches in Chemistry, Springer-Verlag, Berlin, (2003).
§§ Hugh M. Cartwright (ed.), Intelligent Data Analysis in Science, Volume 4 in the Oxford Chemistry Masters Series, Oxford University Press, (2000). ISBN 0-19-850233-8.
§§ Hugh M. Cartwright, Applications of Artificial Intelligence in Chemistry, Volume 11 in the Oxford Chemistry Primers Series, Japanese translation, Maruzen Co, Tokyo, (1995). ISBN 4-621-04088-X
§§ Hugh M. Cartwright, Applications of Artificial Intelligence in Chemistry, Volume 11 in the Oxford Chemistry Primers Series, Oxford University Press, (1993). ISBN 0-19-855736-1.
Some Representative Papers:
§§ Hugh M. Cartwright, Teodora Rusu & Mariana Pinteala, Artificial Intelligence methods: Challenge in computer based polymer design, in G Maroulis, TE Simos (eds.), Computational methods in science and engineering, Amer. Inst. of Physics, (2009), 214-21
§§ Hugh M. Cartwright, Development and uses of Artificial Intelligence in chemistry, in Reviews in Computational Chemistry, Kenny Lipkowitz (ed.), Wiley-VCH, (2007), 349-390.
§§ Hugh M. Cartwright, Les Sztandera & Rohan M. Gunatillake, Genetic algorithms and neural networks in the molecular design of novel fibres, 2nd DEXA workshop: Philosophies and Methodologies for knowledge discovery, deployment and development of decision support systems, Krakow, Poland, (2006).
§§ Hugh M. Cartwright, Les Sztandera & C-C Chu, Genetic Algorithms in Molecular Design of Novel Fibers, NTC Annual Report, (2005).
§§ Les Sztandera, C-C Chu & Hugh M. Cartwright, Genetic Algorithms in Molecular Design of Novel Fibers, NTC Research Briefs, (2005).
§§ Hugh M. Cartwright, Introduction to Evolutionary Computation and Evolutionary Algorithms, in Application of Evolutionary Computation to Chemistry, Roy Johnston (ed.), Springer-Verlag, Heidelberg, (2004).
§§ Hugh M. Cartwright & David Issott, Genetic Algorithm Evolution of Fuzzy Production Rules for the On-line Control of Phenol-Formaldehyde Resin Plants, in Soft Computing Approaches in Chemistry, Hugh M. Cartwright, Les M. Sztandera (eds), Springer-Verlag, Berlin, (2003), 237-26
§§ Hugh M. Cartwright, Undergraduate Projects in the Application of Artificial Intelligence to Chemistry. I. Background, Chemical Educator, 4, (1999), 3-6.
§§ Hugh M. Cartwright, Ben Jesson & Les M. Sztandera, Intelligent algorithmic interpretation of pollutant discharges from multi-unit industrial complexes. Int. J. Intelligent Systems, 12, (1997), 655-672.
§§ Hugh M. Cartwright & Ben Jesson, The analysis of waste flow data from multi-unit industrial complexes using genetic algorithms, Modern Heuristic Search Methods, VJ Rayward-Smith (ed.), John Wiley & Sons Ltd, (1996), 183-197.
§§ Hugh M. Cartwright & Julie A. Hopkins, Evolutionary design of synthetic routes in chemistry. pp 34-48, Proceedings of the AISB Conference on Evolutionary Computing, University of Sussex, Brighton, UK, (1996), 34-48.
§§ Hugh M. Cartwright, Ben Jesson & Les M. Sztandera, Intelligent algorithmic interpretation of waste flow data from multi-unit industrial complexes, Proceedings of the 2nd Conference on Information Sciences, Wrightsville Beach, NC, (1995), 324-327.
§§ Hugh M. Cartwright, The genetic algorithm in science, Pesticide Science, 45, (1995), 171-178.
§§ Hugh M. Cartwright, Getting the timing right - the use of genetic algorithms in scheduling, in Applications of Modern Heuristic Techniques, VJ Rayward-Smith, (ed.), Alfred Waller (1995), 145-156.
§§ Hugh M. Cartwright & Jamie R. Cattell, Studies of continuous-flow chemical synthesis using genetic algorithms, in Applications of Modern Heuristic Techniques, VJ Rayward-Smith, (ed.), Alfred Waller, (1995), 129-144.
§§ Hugh M. Cartwright & Andrew Tuson, Genetic Algorithms and flowshop scheduling: towards the development of a real-time process control system. Lecture Notes in Computer Science, (1994), 277-290.
§§ Hugh M. Cartwright & Robert A. Long, Simultaneous optimization of chemical flowshop sequencing and topology using Genetic Algorithms, Ind. Eng. Chem. Res., 32, (1993), 2706-2713.
DOI: 10.1021/ie00023a037
§§ Hugh M. Cartwright & Stephen P. Harris, Analysis of the distribution of airborne pollution using Genetic Algorithms, Atmos. Environ. A-Gen, 27, (1993), 1783-1791.
§§ Hugh M. Cartwright & Stephen P. Harris, The application of the Genetic Algorithm to 2-dimensional strings – the source apportionment problem, in Steffanie Forrest (Ed.), Proceedings of the 5th international conference on Genetic Algorithms, (1993), 631.
§§ Hugh M. Cartwright & Gregory Mott, Looking around: Using clues from the data space to guide Genetic Algorithm searches, in Rick K. Belew, LB Booker (Eds.), 4th International Conference on Genetic Algorithms, San Mateo, California, Morgan Kaufmann, (1991), 108-114.
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