Towards a genetic theory of easy and hard functions

• Cobb H.G. and Grefenstette J.J. (1993), Genetic algorithms for tracking changing environments, Proceedings of the Fith International Conference on Genetic Algorithms, 1993, S. Forrest (Ed), Morgan Kaufmann, pp 523–530.

  Google Scholar 

• Das R. and Whitley D. (1991), The only challenging problems are deceptive: global search by solving order-1 hyperplane, Proceedings of the Fourth International Conference on Genetic Algorithms, 1991, R.K. Belew and L.B. Booker (Eds), Morgan Kaufmann, pp 166–173.

  Google Scholar 

• Davidor Y. (1990), Epistasis variance: a viewpoint on GA-hardness, Proceedings of the first Workshop on Foundations of Genetic Algorithms, 1990, G.J.E. Rawlins (Ed), Morgan Kaufmann, pp 23–35.

  Google Scholar 

• Davidor Y. and Ben-Kiki O. (1992), The interplay among the genetic algorithm operators: information theory tools used in a holistic way, Proceedings of the Second Conference on Parallel Problem Solving from Nature 1992, R. Manner and B. Manderick (Eds), Elsevier, pp 75–84.

  Google Scholar 

• De Jong K. (1992), Are genetic algorithms function optimizers?, Proceedings of the Second Conference on Parallel Problem Solving from Nature 1992, R. Manner and B. Manderick (Eds), Elsevier, pp 3–13.

  Google Scholar 

• De Jong K., Spears W.M. and Gordon D.F. (1994), Using Markov chains to analyze GAFOs, Proceedings of the third Workshop on Foundations of Genetic Algorithms, 1994.

  Google Scholar 

• Eshelman L.J. and Schaffer J.D. (1992), Real-coded genetic algorithms and interval schemata, Proceedings of the second Workshop on Foundations of Genetic Algorithms, 1992.

  Google Scholar 

• Goldberg D.E. (1987), Simple genetic algorithms and the minimal deceptive problem, Genetic Algorithms and Simulated Annealing, L. Davis (Ed), Morgan Kaufmann, pp 74–88.

  Google Scholar 

• Goldberg D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning: Addison Wesley.

  Google Scholar 

• Goldberg D.E., Deb K. and Korb B. (1991), Don't worry, be messy, Proceedings of the Fourth International Conference on Genetic Algorithms, 1991, R.K. Belew and L.B. Booker (Eds), Morgan Kaufmann, pp 24–30.

  Google Scholar 

• Grefenstette J.J. (1992), Deception considered harmful, Foundations of Genetic Algorithms 2, 1992.

  Google Scholar 

• Hart W.E. and Belew R.K. (1991), Optimizing an arbitrary function is hard for the genetic algorithm, Proceedings of the Fourth International Conference on Genetic Algorithms, 1991, R.K. Belew and L.B. Booker (Eds), Morgan Kaufmann, pp 190–195.

  Google Scholar 

• Holland J.H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

  Google Scholar 

• Jones T. and Forrest S. (1995), Fitness distance correlation as a measure of problem difficulty for genetic algorithms, Proceedings of the Sixth International Conference on Genetic Algorithms, 1995, L.J. Eshelman (Ed), Morgan Kaufmann, pp 184–192.

  Google Scholar 

• Manela M. and Campbell J.A. (1992), Harmonic analysis, epistasis and genetic algorithms, Proceedings of the Second Conference on Parallel Problem Solving from Nature 1992, R. Manner and B. Manderick (Eds), Elsevier, pp 57–64.

  Google Scholar 

• Mitchell M., Forrest S. and Holland J.H. (1991), The royal road for genetic algorithms: fitness landscapes and GA performance, Proceedings of the first European Conference on Artificial Life 1991, F.J. Varela and P. Bourgine (Eds), MIT press/Bradford Books, pp 245–254.

  Google Scholar 

• Mitchell M. and Holland H. (1993), When will a genetic algorithm outperform hill climbing?, Proceedings of the Fith International Conference on Genetic Algorithms, 1993, S. Forrest (Ed), Morgan Kaufmann, pp 647–647.

  Google Scholar 

• Muhlenbein H. (1992), How genetic algorithms really work I.Mutation and hillclimbing, Proceedings of the Second Conference on Parallel Problem Solving from Nature 1992, R. Manner and B. Manderick (Eds), Elsevier, pp 15–25.

  Google Scholar 

• Radcliffe N.J. and Surry P.D. (1994), Fitness variance of formae and performance prediction, Proceedings of the third Workshop on Foundations of Genetic Algorithms, 1994.

  Google Scholar 

• Schaffer J.D., Eshelman L.J. and Offutt D. (1990), Spurious correlation and premature convergence in genetic algorithms, Proceedings of the first Workshop on Foundations of Genetic Algorithms, 1990, G.J.E. Rawlins (Ed), Morgan Kaufmann, pp 102–112.

  Google Scholar 

• Spears W.M., De Jong K.A., Baeck T., Fogel D.B. and de Garis H. (1993), An overview of evolutionary computation, Proceedings of the European Conference on Machine Learning 1993, P. Brazdil (Ed.), Lecture notes in artificial intelligence 667, Springer-Verlag, pp 442–459.

  Google Scholar 

• Syswerda G. (1989), Uniform crossover in genetic algorithms, Proceedings of the third International Conference on Genetic Algorithms, 1989, J.D. Schaffer (Ed), Morgan Kaufmann, pp 2–10.

  Google Scholar 

• Venturini G. (1994), A GA fully deceptive function of order 3 which is also gradient-easy, published in French in the proceedings of Evolution Artificielle 94, Cépaduès Editions.

  Google Scholar 

• Venturini G. (1995), GA consistently deceptive functions are not challenging problems (extended version, unpublished).

  Google Scholar 

• Whitley D. (1989), The genitor algorithm and selective pressure: why rank-based allocation of reproductive trials is best, Proceedings of the third International Conference on Genetic Algorithms, 1989, J.D. Schaffer (Ed), Morgan Kaufmann, pp 116–124.

  Google Scholar 

• Whitley D. (1990), Fundamental principles of deception in genetic search, Proceedings of the first Workshop on Foundations of Genetic Algorithms, 1990, G.J.E. Rawlins (Ed), Morgan Kaufmann, pp 221–241.

  Google Scholar 

• Wilson S.W. (1991), GA-easy does not imply steepest-ascent optimizable, Proceedings of the Fourth International Conference on Genetic Algorithms, 1991, R.K. Belew and L.B. Booker (Eds), Morgan Kaufmann, pp 85–89.

  Google Scholar 

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