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About this product
- DescriptionIn the black box function optimization problem, a search strategy is required to find an extremal point of a function without kwing the structure of the function or the range of possible function values. Solving such problems efficiently requires two abilities. On the one hand, a strategy must be capable of learning while searching: It must gather global information about the space and concentrate the search in the most promising regions. On the other hand, a strategy must be capable of sustained exploration: If a search of the most promising region does t uncover a satisfactory point, the strategy must redirect its efforts into other regions of the space. This dissertation describes a connectionist learning machine that produces a search strategy called stochastic iterated genetic hillclimb- ing (SIGH). Viewed over a short period of time, SIGH displays a coarse-to-fine searching strategy, like simulated annealing and genetic algorithms. However, in SIGH the convergence process is reversible. The connectionist implementation makes it possible to diverge the search after it has converged, and to recover coarse-grained informa- tion about the space that was suppressed during convergence. The successful optimization of a complex function by SIGH usually in- volves a series of such converge/diverge cycles.
- Author(s)David H. Ackley
- PublisherSpringer-Verlag New York Inc.
- Date of Publication17/10/2011
- SubjectComputing: Professional & Programming
- Series TitleThe Springer International Series in Engineering and Computer Science
- Series Part/Volume Number28
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Notebiography
- Weight433 g
- Width155 mm
- Height235 mm
- Spine15 mm
- Edition StatementSoftcover reprint of the original 1st ed. 1987
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