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Genetic algorithms: principles of natural selection applied to computation
1Department of Computer Science, University of New Mexico, Albuquerque 87131-1386.
Science (New York, N.Y.)
|August 13, 1993
Summary
Genetic algorithms mimic computer evolution for problem-solving and modeling natural systems. These search methods, including function optimization and sequence ordering, are increasingly understood and applied.
Area of Science:
- Computer Science
- Artificial Intelligence
- Computational Biology
Background:
- Genetic algorithms are computational models inspired by biological evolution.
- They function as search algorithms applicable to diverse problem domains.
- Recent applications extend to modeling natural evolutionary systems like the immune system.
Purpose of the Study:
- To introduce genetic algorithms as a versatile computational problem-solving tool.
- To highlight their capability in function optimization and sequence ordering.
- To underscore their utility in modeling natural evolutionary processes.
Main Methods:
- Utilizes principles of natural selection and genetics within a computational framework.
- Employs mapping techniques and fitness measures to guide the evolutionary search.
- Applies to optimization problems and sequence determination tasks.
Main Results:
- Demonstrates the adaptability of genetic algorithms to a wide range of problems.
- Shows successful application in function optimization and sequence ordering.
- Confirms their efficacy in modeling complex biological systems.
Conclusions:
- Genetic algorithms offer a powerful approach for solving complex computational problems.
- Mathematical analysis is advancing the understanding and application of these algorithms.
- Their role in computational biology and artificial intelligence is expanding.