Abstract
Recently an abstraction of genetic algorithms has been developed in which a population of GAs in any epoch is represented by a single vector whose elements are the the probabilities of the corresponding bit positions being equivalent to 1. The process of evolution is represented by learning the elements of the probability vector. We have previously extended this to model homeotic genes which are environmentally driven and turn other genes on and off. In this paper we incrementally develop the algorithm on a set of standard problems used to compare methods for the simultaneous optimisation of conflicting criteria within a single population.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm. In Proceedings of the Twelfth International Conference on Machine Learning, 1995.
J. Cohen and I. Stewart. The Collapse of Chaos. Penguin, 1995.
D. Dasgupta and D.R. McGregor. A more motivated genetic algorithm: The model and some results. Cybernetics and Systems, 25(3):447–469, 1994.
C. Fyfe. Developing and understanding niche formation in dynamically-changing environments using structured evolutionary algorithms. In International Symposia on Soft Computing and Intelligent Industrial Automation, 1996.
T. Kohonen. Self-Organising Maps. Springer, 1995.
J.M. Mendel. A Prelude to Neural Networks: Adaptive and Learning Systems. Prentice Hall, 1994. (Ed).
C. Ryan. Racial harmony and function optimization in genetic algorithms-the races genetic algorithm. In Proceedings of EP95. MIT Press, 1995.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Fyfe, C. (1998). Multi-layered Niche Formation. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_42
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive