Practical Genetic Algorithms, Second Edition by Randy L. Haupt, Sue Ellen Haupt(auth.)

By Randy L. Haupt, Sue Ellen Haupt(auth.)

* This ebook offers with the basics of genetic algorithms and their functions in various assorted components of engineering and science
* most vital replace to the second one version is the MATLAB codes that accompany the text
* presents an intensive dialogue of hybrid genetic algorithms
* positive factors extra examples than first editionContent:
Chapter 1 creation to Optimization (pages 1–25):
Chapter 2 The Binary Genetic set of rules (pages 27–50):
Chapter three the continual Genetic set of rules (pages 51–66):
Chapter four easy purposes (pages 67–93):
Chapter five An extra point of class (pages 95–150):
Chapter 6 complex purposes (pages 151–186):
Chapter 7 extra traditional Optimization Algorithms (pages 187–203):

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Extra info for Practical Genetic Algorithms, Second Edition

Example text

The conjugate directions are chosen so that the change in the gradient of the cost function remains perpendicular to the previous direction. 12). If the cost function is not quadratic, then repeating the Nvar iterations several times usually brings the algorithm closer to the minimum. 12 Possible path that a conjugate directions algorithm might take on a quadratic cost surface. 18 INTRODUCTION TO OPTIMIZATION discovered by its four namesakes independently in the mid-1960s (Broyden, 1965; Fletcher, 1963; Goldfarb, 1968; Shanno, 1970).

Thus, although the individuals show great variety, the statistics of the population remain the same. However, we know that few populations are static for very long. When the population is no longer static, the proportion of allele frequencies is no longer constant between generations and evolution occurs. This dynamic process requires an external forcing. The forcing may be grouped into four specific types. (1) Mutations may occur; that is, a random change occurs in the characteristics of a gene.

In general, setting the value to the mid value of the quantization level is best, because the largest error possible is half a level. Rounding the value to the low or high value of 34 THE BINARY GENETIC ALGORITHM the level allows a maximum error equal to the quantization level. 8) In each case = normalized variable, 0 £ pnorm £ 1 plo = smallest variable value phi = highest variable value gene[m] = binary version of pn round{·} = round to nearest integer = quantized version of pnorm pquant qn = quantized version of pn pnorm The binary GA works with bits.

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