UMass Lowell CyberEd
92.419 Computer Algebra with
Mathematica
Kenneth M. Levasseur
Department of Mathematical Sciences
University of Massachusetts Lowell
Lowell, MA 01852
Genetic Algorthims
Subject
Algorithms, Optimization, Computer Science, Mathematics, Biology
Topic
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems.
Reference(s)
- Melanie Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press, 1996. Click here to buy this book from Amazon
Project Idea(s)
If you have some familiarity with genetic algorithms and a specific problem in mind, a Mathematica implementation would be a natural project. In addition to designing an algorithm, you should provide detailed analysis of the results. If you have a good grasp of the process and want to write the project as a tutorial on the subject, that's ok too.
Prerequisite Mathematics
No specific requirements, but good problem-solving skills and intuition for designing functions such as the fitness function that each genetic algorithm is based on is essential.
Required Programming Level
Advanced.
Key Words
Genetic, Evolution, Algorithm, Fitness
Archive
- Kevin Connolly developed a genetic algorithm to find a calling card plan that would optimize profits for a telecommunications firm. Kevin's Project
Reviewer
K. M. Levasseur (
Kenneth_Levasseur@uml.edu)
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