Organisms are one of the most wonderful systems in the world. Like the method of the last chapter, we introduce here another powerful problem solving technique inspired from biology. Genetic algorithm, just like simulated annealing, is suitable to both combinatorial and numerical optimizations. They find wide applications in different research fields, such as management, engineering, industrial design, and so forth.


Genetic Algorithm Simulated Annealing Genetic Programming Travel Salesman Problem Mutation Operation 
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  1. The genetic algorithm was first realized in, J. Holland, “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor (1975)Google Scholar
  2. The following introduces various applications of genetic algorithm, D.E. Goldberg, “Genetic Algorithms in Search, Optimization & Machine Learning”, Addison-Wesley, Reading, Massachusetts (1989)zbMATHGoogle Scholar
  3. Genetic programming was presented by, J. Koza, “Genetic Programming”, MIT Press, Cambridge, MA (1992)zbMATHGoogle Scholar
  4. A review of evolving artificial neural networks is, X. Yao, “Evolving Artificial Neural Networks”, Proceedings of the IEEE, 87(9) (1999) 1423–1447CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Sun-Chong Wang
    • 1
  1. 1.TRIUMFCanada

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