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Unformatted text preview: 0 70 80 90 100 Overview of Performance
TSP30 - Overview of Performance
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.
- Spring '10