Rochester Joint Chapter of the IEEE Computer and Computational Intelligence Societies
Rochester, New York
Date: Monday, December 1, 2014
freeware that is comparable to MATLAB
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Cost: Free. Open to IEEE members and non-members.
Genetic Algorithms (GAs) are among a growing body of problem solving techniques inspired by natural systems, biological, sociological, chemical, physical, etc. GAs are based on the evolutionary idea of survival of the fittest and are implemented as algorithmic problem solving by selective breeding. A GA uses a population of dozens or hundreds of proposed solutions to a problem and repeatedly creates new solutions (children) from pieces of the relatively better individuals (parents), injecting a small amount of error (mutation) into the new individuals. Surprisingly, this often works.
These algorithms can often be very effective to find maxima of continuous functions in cases where calculus cannot easily be applied, and also to quickly find acceptable sub-optimal solutions to difficult (i.e., NP complete) combinatorial problems such as scheduling, bin packing, traveling salesperson, map coloring, etc.
The first of the two talks gives an introduction to the basic algorithm along with variations and tuning parameters and surveys some applications. In this portion, solutions to problems will be represented by bit strings, a familiar object that is easy to create randomly, crossover (sexual reproduction) and mutate.
A second talk, scheduled for the following week, covers more interesting variations and applications. We give special attention to permutation-based problem solutions and how to perform crossovers on permutations.
Peter was a member of the Computer Science faculty at RIT for 25 years, concentrating on graduate education, CS Theory, Neural Networks, Pattern Recognition, and GAs . He continues to actively advise Graduate CS students, pursue research in Fibonacci Numbers, and march with the Pittsford Fire Department Band.
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