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An overview of soft computing, focusing on genetic algorithms (ga's). Soft computing is a problem-solving approach used when the solution permits imprecision and uncertainty. Ga's offer robust results, low cost, and easy tractability, making them beneficial for various applications such as optimization, automatic programming, machine learning, economic models, immune system models, ecological models, and models of social systems. Real-world applications include protein signature selection in mass spectrometry and job shop scheduling problems. The document also covers real-valued optimization and its application to the traveling salesman problem.
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Softcomputing refers to a set of problem solving methods used when the required solution
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Source: http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/tcw2/report.htmlDocsity.com 2
Real valued optimization
Many problems occur as real valued problems, e.g. continuous
Illustration: Ackley’s function
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Application: Protein Signature Selection in Mass Spectrometry
http://www.uni-mainz.de/~frosc000/fbg_po3.html^ molecular weight
relative intensity
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Source: Wendy Williams, web.umr.edu/~ercal/387/slides/GATutorial.pptDocsity.com 8
Crossover
Single point crossover
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Two point crossover
Uniform crossover
Logical crossover
Bit inversion
Source: http://cs.felk.cvut.cz/~xobitko/ga/ Docsity.com
Source: http://cs.felk.cvut.cz/~xobitko/ga/ Docsity.com 11