Matches in SemOpenAlex for { <https://semopenalex.org/work/W2405410209> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W2405410209 abstract "A knowledge-based system is formulated to guide the search strategy selection process in simulation optimization. This system includes a framework for machine learning which enhances the knowledge base and thereby improves the ability of the system to guide optimizations. Response surfaces (i.e., the response of a simulation model to all possible input combinations) are first classified based on estimates of various surface characteristics. Then heuristics are applied to choose the most appropriate search strategy. As the search is carried out and more information about the surface becomes available, the knowledge-based system reclassifies the response surface and, if appropriate, selects a different search strategy. Periodically the system's Learner is invoked to upgrade the knowledge base. Specifically, judgments are made to improve the heuristic knowledge (rules) in the knowledge base (i.e., rules are added, modified, or combined). The Learner makes these judgments using information from two sources. The first source is past experience--all the information generated during previous simulation optimizations. The second source is results of experiments that the Learner performs to test hypotheses regarding rules in the knowledge base.The great benefits of simulation optimization (coupled with the high cost) have highlighted the need for efficient algorithms to guide the selection of search strategies. Earlier work in simulation optimization has led to the development of different search strategies for finding optimal-response-producing input levels. These strategies include response surface methodology, simulated annealing, random search, genetic algorithms, and single-factor search. Depending on the characteristics of the response surface (e.g., presence or absence of local optima, number of inputs, variance), some strategies can be more efficient and effective than others at finding an optimal solution. If the response surface were perfectly characterized, the most appropriate search strategy could, ideally, be immediately selected. However, characterization of the surface itself requires simulation runs. The knowledge-based system formulated here provides an effective approach to guiding search strategy selection in simulation optimization." @default.
- W2405410209 created "2016-06-24" @default.
- W2405410209 creator A5078901965 @default.
- W2405410209 date "1992-01-01" @default.
- W2405410209 modified "2023-09-24" @default.
- W2405410209 title "A knowledge-based simulation optimization system with machine learning" @default.
- W2405410209 hasPublicationYear "1992" @default.
- W2405410209 type Work @default.
- W2405410209 sameAs 2405410209 @default.
- W2405410209 citedByCount "0" @default.
- W2405410209 crossrefType "dissertation" @default.
- W2405410209 hasAuthorship W2405410209A5078901965 @default.
- W2405410209 hasConcept C111919701 @default.
- W2405410209 hasConcept C11413529 @default.
- W2405410209 hasConcept C119857082 @default.
- W2405410209 hasConcept C124101348 @default.
- W2405410209 hasConcept C126661757 @default.
- W2405410209 hasConcept C126980161 @default.
- W2405410209 hasConcept C127705205 @default.
- W2405410209 hasConcept C154945302 @default.
- W2405410209 hasConcept C173801870 @default.
- W2405410209 hasConcept C2780615140 @default.
- W2405410209 hasConcept C41008148 @default.
- W2405410209 hasConcept C4554734 @default.
- W2405410209 hasConcept C81917197 @default.
- W2405410209 hasConcept C98045186 @default.
- W2405410209 hasConceptScore W2405410209C111919701 @default.
- W2405410209 hasConceptScore W2405410209C11413529 @default.
- W2405410209 hasConceptScore W2405410209C119857082 @default.
- W2405410209 hasConceptScore W2405410209C124101348 @default.
- W2405410209 hasConceptScore W2405410209C126661757 @default.
- W2405410209 hasConceptScore W2405410209C126980161 @default.
- W2405410209 hasConceptScore W2405410209C127705205 @default.
- W2405410209 hasConceptScore W2405410209C154945302 @default.
- W2405410209 hasConceptScore W2405410209C173801870 @default.
- W2405410209 hasConceptScore W2405410209C2780615140 @default.
- W2405410209 hasConceptScore W2405410209C41008148 @default.
- W2405410209 hasConceptScore W2405410209C4554734 @default.
- W2405410209 hasConceptScore W2405410209C81917197 @default.
- W2405410209 hasConceptScore W2405410209C98045186 @default.
- W2405410209 hasLocation W24054102091 @default.
- W2405410209 hasOpenAccess W2405410209 @default.
- W2405410209 hasPrimaryLocation W24054102091 @default.
- W2405410209 hasRelatedWork W1550571522 @default.
- W2405410209 hasRelatedWork W1557623003 @default.
- W2405410209 hasRelatedWork W175299670 @default.
- W2405410209 hasRelatedWork W1794398627 @default.
- W2405410209 hasRelatedWork W186881209 @default.
- W2405410209 hasRelatedWork W1996558157 @default.
- W2405410209 hasRelatedWork W2005083491 @default.
- W2405410209 hasRelatedWork W2058542109 @default.
- W2405410209 hasRelatedWork W2106221629 @default.
- W2405410209 hasRelatedWork W2130167399 @default.
- W2405410209 hasRelatedWork W2159383202 @default.
- W2405410209 hasRelatedWork W2287876911 @default.
- W2405410209 hasRelatedWork W2552788977 @default.
- W2405410209 hasRelatedWork W2565688737 @default.
- W2405410209 hasRelatedWork W3007134914 @default.
- W2405410209 hasRelatedWork W3081870453 @default.
- W2405410209 hasRelatedWork W3120856611 @default.
- W2405410209 hasRelatedWork W3138971189 @default.
- W2405410209 hasRelatedWork W3166656282 @default.
- W2405410209 hasRelatedWork W3194102059 @default.
- W2405410209 isParatext "false" @default.
- W2405410209 isRetracted "false" @default.
- W2405410209 magId "2405410209" @default.
- W2405410209 workType "dissertation" @default.