Download Only a cost function is required, no derivatives

Monday February 2, 2004
DSES-4810-01 Intro to COMPUTATIONAL INTELLIGENCE
& SOFT COMPUTING
Instructor:
Office Hours:
Prof. Mark J. Embrechts (x 4009 or 371-4562) ([email protected])
Thursday 10-11 am (CII5217)
Or by appointment.
Class Time:
Monday/Thursday: 8:30-9:50 (Amos eaton hall 216)
TEXT (optional):
J. S. Jang, C. T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing,”
Prentice Hall, 1996. (1998) ISBN 0-13-261066-3
LECTURES 6&7: INTRO to GENETIC ALGORITHMS & EVOLUTIONARY
COMPUTING
Genetic algorithms and evolutionary computing are general-purpose optimization algorithms that
are inspired from biological evolution. Evolutionary computing is the third tier of soft computing
(the others being neural networks and fuzzy logic). First proposed in the sixties by Prof. John
Holland (U. Michigan), they bloomed in the late eighties and the first IEEE journal on evolutionary
computing appeared in 1998. Evolutionary computing is a broader framework than genetic
algorithms.
Genetic algorithms are a derivative free stochastic optimization method (cfr. simulated annealing)
based loosely on the concepts of natural selection and evolutionary processes. Genetic Algorithms
are nowadays often applied in Operations Research applications for rapidly estimating approximate
results.
Genetic algorithms find global optima without relying on gradients, are robust and often provide
surprisingly good results fast. GAs and evolutionary computation have several important industrial
applications (e.g., job scheduling, traveling salesman type of problems) because of the following
desirable characteristics:
Only a cost function is required, no derivatives
Ideal parallel search procedure
Stochastic in nature, less likely to be trapped in local minima
For designing a GA the following steps are necessary:
Encoding scheme
Fitness Function
Selection
Cross-Over
Mutation
Software:
An excellent public domain package for exprt C++ users is GALib
A good freeware MATLAB package is GAOT
A nice, easy to use and simple C code can be ftp’d via:
ftp.uncc.edu, directory coe/evol, file prog.c
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Handout:
1. Philip D. Wasserman, Advanced Methods in Neural Computing, Chapter 5: Genetic Algorithms,
Van Nostrand Reinhold (1993).
Homework #2:
Find an article related to your project and prepare a 1-2 page abstract summarizing the article.
Prepare 10-15 slides (on paper) as if you were going to present the paper. Attach a copy of the paper
and, if relevant, important supporting articles as well. Make sure you reference the paper in the
abstract. Note: the choice of a relevant paper subject and the quality of the selected paper will
influence your grade.
Deadlines
January 22
January 29
February 16
Homework Problem #1 (web browsing)
Project Proposal
Homework Problem #1 (paper)
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