Applied Evolutionary Algorithms in Java pp 101-114 | Cite as
Future Directions in Evolutionary Computing
Chapter
Abstract
As indicated in earlier chapters, all EA methods suffer from some fundamental problems. Prime examples include the computational cost (in processing power and memory requirements), the selection of operators and their parameters is a complex problem in itself, and the complex nature of the EA search space. This chapter considers potential solutions to these issues based on hardware approaches and parallel processing techniques. It also reviews some advanced EA methods that are commonly utilised to address the efficiency of evolution-based search methods.
Keywords
Genetic Programming Multiobjective Optimisation Mobile Agent Artificial Life Evolutionary Computing
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Further Reading
- Back T., Self-adaptation in genetic algorithms, in Self-adaptation in genetic algorithms. In Varela and Bourgine ed., p. 263-271, 1992, Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Cambridge, Artificial Life, MIT Press, 1992.Google Scholar
- Back T., Fogel D.B., & Michalewicz Z. Eds., Evolutionary Computation 2, Advanced Algorithms and Operators, Bristol, UK, Institute of Physics, 2000.Google Scholar
- Grand S., Creation: Life and How to Make It, Weidenfeld; ISBN: 0297643916, 2000.Google Scholar
- Levy S., Artificial Life, Penguin Books; ISBN: 0140231056, 1993.Google Scholar
- Thompson A., An evolved circuit, intrinsic in silicon, entwined with physics, Proc.lst Int.Conf. on Evolvable Systems, ICES’96, ed. Tetsuya Higuchi, Masaya Iwata, L. Weixin, pp. 390-405, Springer-Verlag, LNCS, Vol. 1259, 1997.Google Scholar
- Thompson A., Hardware Evolution Automatic Design of Electronic Circuits in Reconfigurable Hardware by Artificial Evolution, Springer, ISBN: 3-54076253-1, 1998.Google Scholar
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© Springer Science+Business Media New York 2003