Genetic algorithms

ABSTRACT

Genetic algorithms, invented by J. H. Holland, emulate biological evolution in the computer and try to build programs that can adapt by themselves to perform a given function. In some sense, they are analogous to neural networks, but there are important differences between them. This paper shows that genetic algorithms are easy to program, test and analyze by means of APL2 functions.

REFERENCES

• 1: L.J.Fogel, A.J.Owens, M.J.Walsh, Artificial Intelligence through Simulated Evolution. John Wiley and Sons, Inc., New York (1966).
• 2: J.iI.llolland, Adaptation in Natural & Artificial Systems University of Michigan Press (1975).
• 3: J.I {.I lolland, Escaping brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Machine Learning." An AI Approach, Vol. 2. Ed' R.S.Michalski, J.G.Carbonell, T.M.Mitchell. Morgan Kaufman lnc (1986).
• 4: Proceedings of the 1st International Conference on Genetic Algorithms, July 1985
• 5: Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, October 1987
• 6: Lawrence Davis, Genetic Algorithms and Simulated Annealing, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1987
• 7: L.B.Bookcr, D.E.Goldberg, J.{I.Holland, Classitier Systems and Genetic Algorithms. The University of Michigan, Cognitive Science and Machine Intelligence Laboratory, Technical Report 8, 1987.
• 8: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)
      I.2.6 Learning
          Subjects: Connectionism and neural nets

General Terms:
Algorithms, Languages