Matches in SemOpenAlex for { <https://semopenalex.org/work/W1697473400> ?p ?o ?g. }
Showing items 1 to 49 of
49
with 100 items per page.
- W1697473400 endingPage "317" @default.
- W1697473400 startingPage "303" @default.
- W1697473400 abstract "This chapter discusses the performance of a genetic algorithm (GA)-based approach for large-scale molecular design with the help of a large polymer design case study. A bigger problem of polymer design is presented, where the objective of considering the bigger problem is two-fold: primarily, the investigation of the efficacy of the genetic design system for problems with much larger and more complex design spaces, and second to describe the extension of the original GA framework by incorporating higher-level chemical knowledge to enable better handling of constraints, such as chemical stability and molecular complexity. The chapter introduces the large-scale polymer design problem. Results for the standard as well as for the knowledge augmented genetic design framework are presented in the chapter. Some aspects, concerning parametric sensitivity and robustness of Gas, are discussed. It was found that, despite the tremendous increase in the search space size and the complex nonlinear group interactions, the genetic design was generally able to find the target molecules. The chapter concludes by stating that the problem independent, efficient nature of the versatile genetic approach, and the ease with which chemical, biological, design, or process knowledge and constraints can be incorporated make the genetic design framework very appealing for computer-aided molecular design (CAMD) and worthy of further investigation for large-scale molecular design problems." @default.
- W1697473400 created "2016-06-24" @default.
- W1697473400 creator A5024544815 @default.
- W1697473400 creator A5050679262 @default.
- W1697473400 date "2003-01-01" @default.
- W1697473400 modified "2023-10-14" @default.
- W1697473400 title "Polymer Design Case Study" @default.
- W1697473400 cites W1537638769 @default.
- W1697473400 cites W1568537411 @default.
- W1697473400 cites W2041146183 @default.
- W1697473400 cites W2053528014 @default.
- W1697473400 cites W2068110511 @default.
- W1697473400 cites W2075312567 @default.
- W1697473400 cites W2166277125 @default.
- W1697473400 cites W3092190982 @default.
- W1697473400 doi "https://doi.org/10.1016/s1570-7946(03)80015-9" @default.
- W1697473400 hasPublicationYear "2003" @default.
- W1697473400 type Work @default.
- W1697473400 sameAs 1697473400 @default.
- W1697473400 citedByCount "1" @default.
- W1697473400 countsByYear W16974734002014 @default.
- W1697473400 crossrefType "book-chapter" @default.
- W1697473400 hasAuthorship W1697473400A5024544815 @default.
- W1697473400 hasAuthorship W1697473400A5050679262 @default.
- W1697473400 hasConcept C159985019 @default.
- W1697473400 hasConcept C192562407 @default.
- W1697473400 hasConcept C521977710 @default.
- W1697473400 hasConceptScore W1697473400C159985019 @default.
- W1697473400 hasConceptScore W1697473400C192562407 @default.
- W1697473400 hasConceptScore W1697473400C521977710 @default.
- W1697473400 hasLocation W16974734001 @default.
- W1697473400 hasOpenAccess W1697473400 @default.
- W1697473400 hasPrimaryLocation W16974734001 @default.
- W1697473400 hasRelatedWork W2097352990 @default.
- W1697473400 hasRelatedWork W2737498735 @default.
- W1697473400 hasRelatedWork W2744391499 @default.
- W1697473400 hasRelatedWork W2898370298 @default.
- W1697473400 hasRelatedWork W2899084033 @default.
- W1697473400 hasRelatedWork W2908071988 @default.
- W1697473400 hasRelatedWork W2914885646 @default.
- W1697473400 hasRelatedWork W3120461830 @default.
- W1697473400 hasRelatedWork W4292492973 @default.
- W1697473400 hasRelatedWork W4366287736 @default.
- W1697473400 isParatext "false" @default.
- W1697473400 isRetracted "false" @default.
- W1697473400 magId "1697473400" @default.
- W1697473400 workType "book-chapter" @default.