dc.contributor.author |
Brintrup, Alexandra Melike |
|
dc.contributor.author |
Takagi, Hideyuki |
|
dc.contributor.author |
Tiwari, Ashutosh |
|
dc.contributor.author |
Ramsden, Jeremy J. |
|
dc.date.accessioned |
2008-05-07T12:56:28Z |
|
dc.date.available |
2008-05-07T12:56:28Z |
|
dc.date.issued |
2006-09 |
|
dc.identifier.citation |
Alexandra Melike Brintrup, Hideyuki Takagi, Ashutosh Tiwari, Jeremy J. Ramsden; Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems. Journal of Biological Physics and Chemistry, Vol 6 No 3, 2006 pp137-146 |
en_UK |
dc.identifier.issn |
1512-0856 |
|
dc.identifier.uri |
http://hdl.handle.net/1826/2528 |
|
dc.description.abstract |
We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and
parallel IGA, and evaluate them with both simulated and real users. Combining human
evaluation with an optimization system for engineering design enables us to embed domainspecific
knowledge that is frequently hard to describe, i.e. subjective criteria, and design
preferences. We introduce a new IGA technique to extend the previously introduced
sequential single objective GA and multi-objective GA, viz. parallel IGA. Experimental
evaluation of three algorithms with a multi-objective manufacturing plant layout design task
shows that the multi-objective IGA and the parallel IGA clearly provide better results than the
sequential IGA, and that the multi-objective IGA gives the most diverse results and fastest
convergence to a stable set of qualitatively optimum solutions, although the parallel IGA
provides the best quantitative fitness convergence. |
en_UK |
dc.language.iso |
en |
en_UK |
dc.publisher |
Jointly by, Collegium Basilea (Institute of Advanced Study) and Association of Modern Scientific Investigation. |
en_UK |
dc.relation.ispartof |
www.amsi.ge/jbpc |
|
dc.subject |
innovative design |
en_UK |
dc.subject |
subjectivity |
en_UK |
dc.subject |
evolutionary computing |
en_UK |
dc.title |
Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems. |
en_UK |
dc.type |
Article |
en_UK |