Matches in SemOpenAlex for { <https://semopenalex.org/work/W20297910> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W20297910 abstract "We propose a global optimization algorithm called GOSAM (Global Optimization using Support vector regression based Adaptive Multistart) that applies statistical machine learning techniques, viz. Support Vector Regression (SVR) to adaptively direct iterative search in large-scale global optimization. At each iteration, GOSAM builds a training set of the objective function’s local minima discovered till the current iteration, and applies SVR to construct a regressor that learns the structure of the local minima. In the next iteration the search for the local minimum is started from the minimum of this regressor. The idea is that the regressor for local minima will generalize well to the local minima not obtained so far in the search, and hence its minimum would be a ‘crude approximation’ to the global minimum. This approximation improves over time, leading the search towards regions that yield better local minima and eventually the global minimum. Simulation results on well known benchmark problems show that GOSAM requires significantly fewer function evaluations to reach the global optimum, in comparison with methods like Particle Swarm optimization and Genetic Algorithms. GOSAM proves to be relatively more efficient as the number of design variables (dimension) increases. GOSAM does not require explicit knowledge of the objective function, and also does not assume any specific properties. We also discuss some real world applications of GOSAM involving constrained and design optimization problems." @default.
- W20297910 created "2016-06-24" @default.
- W20297910 creator A5002842122 @default.
- W20297910 creator A5034635671 @default.
- W20297910 creator A5083305798 @default.
- W20297910 date "2010-01-01" @default.
- W20297910 modified "2023-10-14" @default.
- W20297910 title "Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy" @default.
- W20297910 cites W1494289067 @default.
- W20297910 cites W1982582601 @default.
- W20297910 cites W2023248353 @default.
- W20297910 cites W2024060531 @default.
- W20297910 cites W2032431794 @default.
- W20297910 cites W2107941094 @default.
- W20297910 cites W2116021187 @default.
- W20297910 cites W2138490239 @default.
- W20297910 cites W2152195021 @default.
- W20297910 cites W2165603655 @default.
- W20297910 cites W2168519934 @default.
- W20297910 cites W4235178178 @default.
- W20297910 cites W4239256746 @default.
- W20297910 doi "https://doi.org/10.1007/978-3-642-12775-5_6" @default.
- W20297910 hasPublicationYear "2010" @default.
- W20297910 type Work @default.
- W20297910 sameAs 20297910 @default.
- W20297910 citedByCount "1" @default.
- W20297910 countsByYear W202979102016 @default.
- W20297910 crossrefType "book-chapter" @default.
- W20297910 hasAuthorship W20297910A5002842122 @default.
- W20297910 hasAuthorship W20297910A5034635671 @default.
- W20297910 hasAuthorship W20297910A5083305798 @default.
- W20297910 hasConcept C12267149 @default.
- W20297910 hasConcept C126255220 @default.
- W20297910 hasConcept C13280743 @default.
- W20297910 hasConcept C134306372 @default.
- W20297910 hasConcept C135320971 @default.
- W20297910 hasConcept C154945302 @default.
- W20297910 hasConcept C164752517 @default.
- W20297910 hasConcept C177264268 @default.
- W20297910 hasConcept C185798385 @default.
- W20297910 hasConcept C186633575 @default.
- W20297910 hasConcept C199360897 @default.
- W20297910 hasConcept C202444582 @default.
- W20297910 hasConcept C205649164 @default.
- W20297910 hasConcept C33676613 @default.
- W20297910 hasConcept C33923547 @default.
- W20297910 hasConcept C41008148 @default.
- W20297910 hasConcept C85617194 @default.
- W20297910 hasConceptScore W20297910C12267149 @default.
- W20297910 hasConceptScore W20297910C126255220 @default.
- W20297910 hasConceptScore W20297910C13280743 @default.
- W20297910 hasConceptScore W20297910C134306372 @default.
- W20297910 hasConceptScore W20297910C135320971 @default.
- W20297910 hasConceptScore W20297910C154945302 @default.
- W20297910 hasConceptScore W20297910C164752517 @default.
- W20297910 hasConceptScore W20297910C177264268 @default.
- W20297910 hasConceptScore W20297910C185798385 @default.
- W20297910 hasConceptScore W20297910C186633575 @default.
- W20297910 hasConceptScore W20297910C199360897 @default.
- W20297910 hasConceptScore W20297910C202444582 @default.
- W20297910 hasConceptScore W20297910C205649164 @default.
- W20297910 hasConceptScore W20297910C33676613 @default.
- W20297910 hasConceptScore W20297910C33923547 @default.
- W20297910 hasConceptScore W20297910C41008148 @default.
- W20297910 hasConceptScore W20297910C85617194 @default.
- W20297910 hasLocation W202979101 @default.
- W20297910 hasOpenAccess W20297910 @default.
- W20297910 hasPrimaryLocation W202979101 @default.
- W20297910 hasRelatedWork W1549015662 @default.
- W20297910 hasRelatedWork W1586244874 @default.
- W20297910 hasRelatedWork W1604926140 @default.
- W20297910 hasRelatedWork W2001014165 @default.
- W20297910 hasRelatedWork W2020667009 @default.
- W20297910 hasRelatedWork W2036302761 @default.
- W20297910 hasRelatedWork W2036596201 @default.
- W20297910 hasRelatedWork W2047953186 @default.
- W20297910 hasRelatedWork W2097341281 @default.
- W20297910 hasRelatedWork W2122919994 @default.
- W20297910 hasRelatedWork W2166486811 @default.
- W20297910 hasRelatedWork W2279373278 @default.
- W20297910 hasRelatedWork W2285921046 @default.
- W20297910 hasRelatedWork W2362515065 @default.
- W20297910 hasRelatedWork W2381885343 @default.
- W20297910 hasRelatedWork W2383986156 @default.
- W20297910 hasRelatedWork W2782624216 @default.
- W20297910 hasRelatedWork W2793946684 @default.
- W20297910 hasRelatedWork W28895184 @default.
- W20297910 hasRelatedWork W3199281429 @default.
- W20297910 isParatext "false" @default.
- W20297910 isRetracted "false" @default.
- W20297910 magId "20297910" @default.
- W20297910 workType "book-chapter" @default.