By Whitley D.
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Hyperplane sampling requires larger populations. Small populations are much more likely to rely on hill-climbing. A population of 20 individuals just doesn't provide very much information about hyperplane partitions, except perhaps very low order hyperplanes (there are only 5 samples of each order-2 hyperplane in a population of 20). Second, very high selective pressure suggests hill-climbing may dominate the search. If the 5 best individuals in a population of 100 strings reproduce 95% of the time, then the e ective population size may not be large enough to support hyperplane sampling.
34 11 Conclusions One thing that is striking about genetic algorithms and the various parallel models is the richness of this form of computation. What may seem like simple changes in the algorithm often result in surprising kinds of emergent behavior. Recent theoretical advances have also improved our understanding of genetic algorithms and have opened to the door to using more advanced analytical methods. Many other timely issues have not been covered in this tutorial. In particular, the issue of deception has not been discussed.
Biological Cybernetics, 62:397-405. Goldberg, D. (1987) Simple Genetic Algorithms and the Minimal, Deceptive Problem. In, Genetic Algorithms and Simulated Annealing, L. , Pitman. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley. Goldberg, D. (1990) A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. TCGA 90003, Engineering Mechanics, Univ. Alabama. Goldberg, D. (1991) The Theory of Virtual Alphabets.
A genetic algorithm tutorial by Whitley D.