people at microsoft wrote a program that uses some

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Unformatted text preview: 0 70 80 90 100 Overview of Performance TSP30 - Overview of Performance 1800 1600 1400 1200 e c n a t s i D 1000 800 600 400 200 0 Best 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Generations (1000) Worst Average Considering the GA Technology “Almost eight years ago ... people at Microsoft wrote a program [that] uses some genetic things for finding short code sequences. Windows 2.0 and 3.2, NT, and almost all Microsoft applications products have shipped with pieces of code created by that system.” - Nathan Myhrvold, Microsoft Advanced Technology Group, Wired, September 1995 Issues for GA Practitioners Choosing basic implementation issues: representation population size, mutation rate, ... selection, deletion policies crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function (often hardest part) Benefits of Genetic Algorithms Concept is easy to understand Modular, separate from application Supports multi­objective optimization Good for “noisy” environments Always an answer; answer gets better with time Inherently parallel; easily distributed Many ways to speed up and improve a GA­based application as knowledge about problem domain is gained Easy to exploit previous or alternate solutions Flexible building blocks for hybrid applications Substantial history and range of use When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Prob...
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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