By Randy L. Haupt, Sue Ellen Haupt

This well-organized, introductory textual content takes the reader in the course of the new and speedily increasing box of genetic algorithms step-by-step, from a dialogue of numerical optimization, to a survey of present extensions to genetic algorithms and functions. For the 1st time, thought is associated with useful program in quite a few disciplines throughout the presentation of various examples. encompasses a word list of significant phrases and an inventory of a few genetic set of rules workouts in pseudocode. --This textual content refers to an out of print or unavailable variation of this name.

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**Additional info for Practical Genetic Algorithms (2nd Edition)**

**Sample text**

Does the transformation used affect the speed of convergence? 1 GENETIC ALGORITHMS: NATURAL SELECTION ON A COMPUTER If the previous chapter whet your appetite for something better than the traditional optimization methods, this and the next chapter give step-by-step procedures for implementing two ﬂavors of a GA. Both algorithms follow the same menu of modeling genetic recombination and natural selection. One represents variables as an encoded binary string and works with the binary strings to minimize the cost, while the other works with the continuous variables themselves to minimize the cost.

Darwin reﬁned his ideas during his voyage as naturalist on the Beagle, especially during his visits to the Galapagos Islands. Darwin’s theory of evolution was based on four primary premises. First, like begets like; equivalently, an offspring has many of the characteristics of its parents. This premise implies that the population is stable. Second, there are variations in characteristics between individuals that can be passed from one generation to the next. The third premise is that only a small percentage of the offspring produced survive to adulthood.

Borowski, E. , and J. M. Borwein. 1991. Mathematics Dictionary. New York: HarperCollins. Box, M. J. 1965. A comparison of several current optimization methods and the use of transformations in constrained problems. Comput. J. 8:67–77. Boyer, C. , and U. C. Merzbach. 1991. A History of Mathematics. New York: Wiley. Broyden, G. C. 1965. A class of methods for solving nonlinear simultaneous equations. Math. Comput. 19:577–593. Curtis, H. 1975. Biology, 2nd ed. New York: Worth. Cuthbert, T. R. Jr. 1987.