A Connectionist Machine for Genetic Hillclimbing:
In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing the structure of the function or the range of possible function values. Solving such problems efficiently requires two abilities. On the one hand, a strat...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1987
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
28 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing 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 not 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 |
| Beschreibung: | 1 Online-Ressource (XIV, 260 p) |
| ISBN: | 9781461319979 |
| DOI: | 10.1007/978-1-4613-1997-9 |
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| 520 | |a In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing 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 not 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 | ||
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Datensatz im Suchindex
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4613-1997-9 |
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| spelling | Ackley, David H. Verfasser aut A Connectionist Machine for Genetic Hillclimbing by David H. Ackley Boston, MA Springer US 1987 1 Online-Ressource (XIV, 260 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 28 In the "black box function optimization" problem, a search strategy is required to find an extremal point of a function without knowing 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 not 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 Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Künstliche Intelligenz (DE-588)4033447-8 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461291923 https://doi.org/10.1007/978-1-4613-1997-9 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Ackley, David H. A Connectionist Machine for Genetic Hillclimbing Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4113937-9 |
| title | A Connectionist Machine for Genetic Hillclimbing |
| title_auth | A Connectionist Machine for Genetic Hillclimbing |
| title_exact_search | A Connectionist Machine for Genetic Hillclimbing |
| title_full | A Connectionist Machine for Genetic Hillclimbing by David H. Ackley |
| title_fullStr | A Connectionist Machine for Genetic Hillclimbing by David H. Ackley |
| title_full_unstemmed | A Connectionist Machine for Genetic Hillclimbing by David H. Ackley |
| title_short | A Connectionist Machine for Genetic Hillclimbing |
| title_sort | a connectionist machine for genetic hillclimbing |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Künstliche Intelligenz Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4613-1997-9 |
| work_keys_str_mv | AT ackleydavidh aconnectionistmachineforgenetichillclimbing |