Book Chapter10.1016/B978-1-55860-036-2.50105-3
An incremental genetic algorithms for real-time learning
Terence C. Fogarty
- 01 Dec 1989
- pp 416-419
21
TL;DR: An incremental genetic algorithm which generates only one new member of the population and deletes only old one at a time thus equalizing the amount of computation and learning at each time interval is introduced.
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Abstract: The genetic algorithm, operated in batch mode, evaluates the whole population in some environment and generates through selection, crossover and mutation a new population. In a real-time learning situation, where the population can only be evaluated sequentially, much of the computation and all of the learning is concentrated into one time interval between the evaluation of the last member of the old population and the generation of the first member of the new. This paper introduces an incremental genetic algorithm which generates only one new member of the population and deletes only old one at a time thus equalizing the amount of computation and learning at each time interval. It then compares the performance of the incremental and non-incremental genetic algorithms and of a rule based system for optimising combustion on ten simulations of multiple burner installations.
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Citations
Transposon Element Technique Applied to GA-Based John Muir's Trail Test
Alexander V. Spirov,A. S. Kadyrov +1 more
- 21 Apr 1998
TL;DR: A new adaptive learning evolutionary algorithm - parallel transposon element technique (PTET), based on invasion of evolving genomes by parasitic/selfish mobile genetic elements, which can be tested both for smooth and rugged and/or multiply connected fitness landscapes.
31
Genetics-based machine learning
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- 01 Jan 2010
TL;DR: This is a survey of the field of Genetics-based Machine Learning: the application of evolutionary algorithms to machine learning, with emphasis on their evolutionary aspects.
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Adaptive Representations for Reinforcement Learning
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- 05 Oct 2010
TL;DR: This thesis introduces two novel approaches for automatically discovering high-performing representations and presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.
Development of parallel hybrid optimisation techniques based on genetic algorithms and simulated annealing
Kit Po Wong,S. Yin Wa Wong +1 more
- 01 Jan 1994
TL;DR: In this article, coarse-grained parallel algorithms for hybrid optimisation techniques based on genetic algorithms and simulated annealing were developed for a short-term hydrothermal scheduling problem.
14
Development of Parallel Hybrid Optimisation Techniques Based on Genetic Algorithms and Simulated Annealing
Kit Po Wong,S. Yin Wa Wong +1 more
- 16 Nov 1993
TL;DR: In this paper, coarse-grained parallel algorithms for hybrid optimisation techniques based on genetic algorithms and simulated annealing were developed for a short-term hydrothermal scheduling problem.
11
References
Optimization of Control Parameters for Genetic Algorithms
John J. Grefenstette
- 01 Jan 1986
TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
3.1K
Adaptive System Design: A Genetic Approach
Kenneth De Jong
- 01 Sep 1980
TL;DR: An unconventional approach to adaptive system design is presented which has considerable promise for complex automation problems and results are presented which suggest that reproductive plans out-perform existing techniques on complex process response surfaces.
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