Parallel Genetic Algorithms: An Efficient Model and Applications in Control Systems
Genetic algorithms are search and optimisation methods based on the principle of
natural selection. They provide promising results in non-linear and complex search
problems. They have been proven to be parallel and global in nature. However, genetic
algorithms run slowly on sequential machines. The reason for that is that the
sequential computation does not reflect the true spatial structure of the algorithm.
This research has developed a new parallel model of genetic computation which improves
on existing parallel models and is suitable for real-time
applications in system identification and intelligent control.
Using the Markov chain formalism the model has been shown to be globally
convergent. The salient features of this new model are:
- independence of the optimisation problem; - ability to cope
with a black box problem; - suitability for real-time applications in which the
exact model of the system is unknown; and implementability within the
current technological framework. The details of the model are provided in a separate publication
[Muhammad, Bargiela, King, 1997].
Gradient like information has been integrated into the genetic search in order
to improve the performance and efficiency of the algorithm. A novel directional search
method has been developed and shown to result in significantly improved
performance of genetic optimisation.
Unlike neural networks and fuzzy systems, genetic algorithms do not provide
any general logic for system modelling. Therefore system identification is achieved by
means of fuzzy network for general logic and a genetic algorithm for parameter
estimation giving as a result an evolving fuzzy network. This novel method has been
applied to modelling of chaotic time series and it has been used to control a highly
non-linear system, i.e. inverse pendulum. It is expected that with the advance of
re-configurable electronics, evolutionary chips will be realised in the near future
and they will fully exploit the potential of the genetic algorithms based
- Muhammad A., Bargiela A., King G., Fine-Grained Parallel Genetic Algorithm: A Stochastic Optimisation Method, Proc. of 1st World Congress on Systems Simulation, Singapore, Sept. 1997, ISBN 1-56555-114-1, pp.199-203, PDF
- Muhammad A., Bargiela A. King G., Fine-Grained Genetic Algorithm: A Global Convergence Criterion, Int. Journal of Computer Mathematics , Vol.73, No. 2, 1999, pp.139-155, doi:10.1080/00207169908804885, PDF
- Muhammad A., Bargiela A., King G., Fuzzy and Evolutionary Modelling of Non-Linear Control Systems, Mathematical and Computer Modelling , vol. 33, pp 533-551, 2001, doi:10.1016/S0895-7177(00)00259-4, PDF
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- Hartley J., Bargiela A., Cant R., Parallel simulation of large scale water distribution systems, Proceedings of Modelling and Simulation Conference ESM’95, Prague, June 1995, ISBN 1-56555-080-3, pp. 723-727., PDF
- Hartley J., Bargiela A., XTPML - Simplifying the Development of Parallel Programs for Implementation on Various Transputer Architectures, Proc. European Simulation Symposium ESS’98, Oct. 1998, ISBN 1-56555-147-8, pp.119-123., PDF
- Bargiela A., Parallel and distributed telemetry data processing, Proceedings of Parallel Computing and Transputers Conference, PCAT’93, Brisbaine, 1993, ISBN 90 5199 1495, pp. 269-275.
- Bargiela A., Hosseinzaman A., Parallel simulation of nonlinear networks using diakoptics, Proceedings of Int. Conf. on Parallel Computing and Transputer Applications PACTA ‘92, (Eds.) M Valero at.all, Barcelona, Sept. 1992, ISBN 84 87867 138, pp. 1463-1473
- Bargiela A., Nonlinear network tearing algorithm for transputer system implementation, Proc. of Int. Conf. TAPA-92, Melbourne, November 1992, ISBN 905199115 0, pp. 19-24.
Last update: 11/11/97