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Reproductive adaptive plans

Published: 01 August 1972 Publication History

Abstract

This paper traces the experimental development of a new class of powerful and flexible adaptive plans, called reproductive plans. Adaptive plans are formally presented as search procedures for locating superior devices in an extremely large space. Reproductive adaptive plans operate by treating the search procedure as an evolutionary process of finding the best organism in a certain environment. Devices are represented as strings or chromosomes. At each “generation” or time step a population of devices is tested and each device is copied (rewarded) according to its performance. Then the copies “mate” using a number of genetic-like operators to produce a modified population of devices. In this particular study devices are pattern recognition programs although they could be any set of modifiable procedures. Much of the work is concerned with experimentally testing and improving the general reproductive plan to achieve fast and continuous adaptation.

References

[1]
Holland, J. H., "Nonlinear Environments Permitting Efficient Adaptation" in Computer and Information Sciences-II, 1967, Academic Press, New York]]
[2]
Newell, A. and Simon, H. A., "GPS, A Program that Simulates Human Thought," in Computers and Thought, Feigenbaum, E. A. and Feldman J., ed., 1963, McGraw Hill, New York]]
[3]
Newell, A., Shaw, J. C., and Simon, H. A., "A Variety of Intelligent Learning in a General Problem Solver," in Self-Organizing Systems, Yovitts, M. and Cameron, S., ed., 1960, Pergamon Press, New York]]
[4]
Samuel, A. L., "Some Studies in Machine Learning Using the Game of Checkers," in Computers and Thought, Feigenbaum, E. A., and Feldman, J., ed., 1963, McGraw Hill, New York]]
[5]
Klopf, A. H., Evolutionary Pattern Recognition Systems, 1965, Bioengineering Section Report, Information Engineering Department, The University of Illinois, Chicago, Illinois]]
[6]
Fogel, L. J., Owen, A. J., and Walsh, M. F., Artificial Intelligence Through Simulated Evolution, 1966, Wiley, New York]]
[7]
Cavicchio, D. J., Adaptive Search Using Simulated Evolution, Report 03296-4-T, 1970, Computer and Communication Sciences Department, University of Michigan, Ann Arbor, Michigan]]
[8]
Holland, J. H., "Adaptive Plans Optimal for Payoff-Only Environments," Proceedings of the Second Hawaii Conference on Systems Sciences, University of Hawaii, Honolulu, January 22-24, 1969, Periodicals, 1969, North Hollywood, California]]
[9]
Bagley, J. D., The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms, Report 01252-1-T, 1967, Computer and Communications Sciences Department, University of Michigan, Ann Arbor, Michigan]]
[10]
Holland, J. H., Forthcoming book on adaptive systems tentatively titled: Adaptation in Natural and Artificial Systems.]]
[11]
Bledsoe, W. W. and Browning, I., "Pattern Recognition and Reading by Machine," Proceedings of the Eastern Joint Computer Conference, Boston, Massachusetts, December 1-3, 1959, National Joint Computer Committee, Association for Computing Machinery, 1959, New York]]
[12]
Uhr, L. and Vossler, C., "A Pattern Recognition Program that Generates, Evaluates, and Adjusts Its Own Operators," in Computers and Thought, Feigenbaum, E. A. and Feldman, J., ed., 1963, McGraw Hill, New York]]

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cover image ACM Conferences
ACM '72: Proceedings of the ACM annual conference - Volume 1
August 1972
194 pages
ISBN:9781450374910
DOI:10.1145/800193
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 1972

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Author Tags

  1. Adaptation
  2. Adaptive plans
  3. Adaptive search
  4. Adaptive systems
  5. Artificial intelligence
  6. Evolution
  7. Heuristic search
  8. Learning
  9. Pattern recognition
  10. Reproductive adaptive plans
  11. Search procedures

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August 1, 1972
Massachusetts, Boston, USA

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  • (2020)Multi-strategy synergy-based backtracking search optimization algorithmSoft Computing10.1007/s00500-020-05225-8Online publication date: 5-Aug-2020
  • (2018)Genetic Operators in Evolutionary Music Composition2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC.2018.00047(253-259)Online publication date: Sep-2018
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  • (2009)Parameter Identification within a Porous Medium using Genetic AlgorithmsHandbook of Porous Media, Second Edition10.1201/9780415876384.ch17(678-742)Online publication date: 11-Dec-2009
  • (2008)Research on Diversity Measure of Niche Genetic AlgorithmProceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing10.1109/WGEC.2008.66(47-50)Online publication date: 25-Sep-2008
  • (2008)Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAsProceedings of the 2008 Fourth International Conference on Natural Computation - Volume 0110.1109/ICNC.2008.532(580-585)Online publication date: 18-Oct-2008
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