Genetic algorithms

Genetic algorithms or GAs view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.

References

• Barricelli, Nils Aall (1954), Esempi numerici di processi di evoluzione, Methodos, pp. 45-68.
• Barricelli, Nils Aall (1963), Numerical testing of evolution theories. Part II. Preliminary tests of performance, symbiogenesis and terrestrial life, Acta Biotheoretica, 16: 99-126.
• Bies, Robert R; Muldoon, Matthew F; Pollock, Bruce G; Manuck, Steven; Smith, Gwenn and Sale, Mark E(2006), A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection Journal of Pharmacokinetics and Pharmacodynamics Springer-Netherlands pp. 196-221
• Crosby, Jack L. (1973), Computer Simulation in Genetics, John Wiley & Sons, London.
• Falkenauer, Emanuel (1997), Genetic Algorithms and Grouping Problems, John Wiley & Sons Ltd, Chichester, England. ISBN 978-0-471-97150-4
• Fentress, Sam W (2005), Exaptation as a means of evolving complex solutions, MA Thesis, University of Edinburgh. (pdf)
• Fogel, David B. (2000) Evolutionary Computation: Towards a New Philosophy of Machine Intelligence IEEE Press, New York.
• Fogel, David B. (editor) (1998) Evolutionary Computation: The Fossil Record, IEEE Press, New York.
• Fraser, Alex S. (1957), Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction. Australian Journal of Biological Sciences vol. 10 484-491.
• Fraser, Alex and Donald Burnell (1970), Computer Models in Genetics, McGraw-Hill, New York.
• Goldberg, David E (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, Boston, MA.
• Goldberg, David E (2002), The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Addison-Wesley, Reading, MA.
• Holland, John H (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor
• Kjellström, G. Optimization of electrical Networks with respect to Tolerance Costs. Ericsson Technics, no. 3, pp. 157-175, 1970.
• Kjellström, G. Evolution as a statistical optimization algorithm. Evolutionary Theory 11:105-117 (January, 1996).
• Koza, John (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press. ISBN 0-262-11170-5
• Michalewicz, Zbigniew (1999), Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag.
• Mitchell, Melanie, (1996), An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA.
• Rechenberg, Ingo (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
• Schmitt, Lothar M, Nehaniv Chrystopher N, Fujii Robert H (1998), Linear analysis of genetic algorithms, Theoretical Computer Science (208), pp. 111-148
• Schmitt, Lothar M (2001), Theory of Genetic Algorithms, Theoretical Computer Science (259), pp. 1-61
• Schmitt, Lothar M (2004), Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling, Theoretical Computer Science (310), pp. 181-231
• Schwefel, Hans-Paul (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977).
• Syswerda G. (1989) Uniform crossover in genetic algorithms. In J. D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann.
• Vose, Michael D (1999), The Simple Genetic Algorithm: Foundations and Theory, MIT Press, Cambridge, MA.
• Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing 4, 65–85.
• Wright, A.H. et al. (2003) Implicit Parallelism in Proceedings of the Genetic and Evolutionary Computation Conference 2003

External links

• A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University An excellent tutorial with lots of theory
• Another tutorial
• Another tutorial