Implicit formae in genetic algorithms

  • Márk Jelasity
  • József Dombi
Theoretical Foundations of Evolutionary Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1141)


This paper discusses the term implicit forma, which is useful for explaining the behaviour of genetic algorithms. Implicit formae are predicates over the chromosome space that are not strongly connected to the representation at hand but are capable of directing the search. After a short theoretical discussion, three examples are given for illustration, including the subset sum problem which is NP-complete.


Objective Function Genetic Algorithm Problem Instance Genetic Operator Relevance Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    J.H. Holland (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press (Ann Arbor).Google Scholar
  2. 2.
    N.J. Radcliff (1991) Equivalence Class Analysis of Genetic Algorithms. Complex Systems, 5(2):183–205Google Scholar
  3. 3.
    M.D. Vose (1991) Generalizing the Notion of Schemata in Genetic Algorithms. Artificial Intelligence Google Scholar
  4. 4.
    N.J. Radcliff (1992) Non-linear Genetic Representations. In R. Männer and B. Manderick editors, Parallel Problem Solving from Nature 2. pp259–268, Elsevier Science Publishers/North Holland (Amsterdam)Google Scholar
  5. 5.
    S. Khuri, T. Bäck, J. Heitkötter (1993) An Evolutionary Approach to Combinatorial Optimization Problems, in The Proceedings of CSC'94.Google Scholar
  6. 6.
    M. Mitchell, J.H. Holland (1993) When will a Genetic Algorithm Outperform Hill-climbing? (SFI working paper)Google Scholar
  7. 7.
    N.J. Radcliff, F.A.W. George (1993) A Study in Set Recombination. In The Proceedings of ICGA '93.Google Scholar
  8. 8.
    A. Juels, M. Wattenberg (1994) Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms. Technical Report, UC BerkeleyGoogle Scholar
  9. 9.
    N.J. Radcliffe, P.D. Surry (1994) Fitness Variance of Formae and Performance Prediction. In L.D. Whitley and M.D. Vose editors, Foundations of Genetic Algorithms III, Morgan Kaufmann (San Mateo, CA) pp51–72Google Scholar
  10. 10.
    H. Kargupta (1995) Signal-to-noise, Croostalk and Long Range Difficulty in Genetic Algorithms. In The Proceedings of ICGA '95.Google Scholar
  11. 11.
    M.S White, S.J. Flockton (1995) Modeling the Behaviour of the Genetic Algorithm. In The proceedings of GALESIA '95 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Márk Jelasity
    • 1
  • József Dombi
    • 2
  1. 1.Student of József Attila UniversitySzegedHungary
  2. 2.Department of Applied InformaticsJózsef Attila UniversitySzegedHungary

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