A Field Guide to Genetic ProgrammingLulu.com, 2008 - 233 lappuses Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book. |
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1.–5. rezultāts no 42.
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... objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate . . . . . . . . . . . . . . . . 76 9.2.1 Multi-objective Bloat and Complexity Control . . . . 77 ...
... objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate . . . . . . . . . . . . . . . . 76 9.2.1 Multi-objective Bloat and Complexity Control . . . . 77 ...
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Saturs
Introduction | 1 |
I Basics | 7 |
II Advanced Genetic Programming | 37 |
III Practical Genetic Programming | 109 |
IV Tricks of the Trade | 143 |
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Advances algorithms applications approach automatically bloat chapter common Computer Science Conference constant created crossover depth distribution editors effects et al evaluation evolution Evolutionary Computation evolved example execution Figure fitness fitness function function Genetic Algorithms Genetic and Evolutionary genetic programming GP system GPBiB IEEE implementation important individuals initial Intelligence International ISBN ISSN J. R. Koza July June learning limit linear LNCS machine method Morgan Kaufmann mutation Nature node Notes objectives operators parallel parent performance Poli population possible Practice Press primitive probability problem Proceedings produce random randomly representation represented selection simple solution solve SP SP SP space Springer Springer-Verlag structure subtree techniques terminal theory tion tree typically University variables volume W. B. Langdon