genetic Algorithms (gas) Are Powerful Search Techniques Based On Principles Of Evolution And Widely Applied To Solve Problems In Many Disciplines. However, Most Gas Employed In Practice Nowadays Are Unable To Learn Genetic Linkage And Suffer From The Linkage Problem. The Linkage Learning Genetic Alg
β¦ LIBER β¦
Convergence Time for the Linkage Learning Genetic Algorithm
β Scribed by Chen, Ying-ping; Goldberg, David E.
- Book ID
- 120487396
- Publisher
- MIT Press
- Year
- 2005
- Tongue
- English
- Weight
- 243 KB
- Volume
- 13
- Category
- Article
- ISSN
- 1063-6560
No coin nor oath required. For personal study only.
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In this paper we consider the extension of genetic algorithms (GAs) with a probabilistic Boltzmann reduction operator and prove their convergence to the optimum. The algorithm can be seen as a hybridisation between GAs and simulated annealing (SA), i.e. a SA-like GA. The "temperature" parameter allo