Abstract
We propose a sequential IGA, multi-objective IGA and parallel interactive genetic algorithm (IGA), and evaluate them with a multi-objective floor planning task through both simulation and real IGA users. Combining human evaluation with an optimization system for engineering design enables us to embed domain specific knowledge which is frequently hard to describe, subjective criteria and preferences in engineering design. We introduce IGA technique to extend previous approaches with sequential single objective GA and multi-objective GA. We also introduce parallel IGA newly. Experimental results show that (1) the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and (2) the multi-objective IGA provides more diverse results and faster convergence for a floor planning task although the parallel IGA provides better fitness convergence.
| Original language | English |
|---|---|
| Title of host publication | Applications of Evolutionary Computing - EvoWorkshops 2006 |
| Subtitle of host publication | EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Proceedings |
| Pages | 586-598 |
| Number of pages | 13 |
| DOIs | |
| Publication status | Published - Jul 14 2006 |
| Event | EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC - Budapest, Hungary Duration: Apr 10 2006 → Apr 12 2006 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3907 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 4/10/06 → 4/12/06 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective floor plan optimisation. / Brintrup, Alexandra Melike; Takagi, Hideyuki; Ramsden, Jeremy.
Applications of Evolutionary Computing - EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Proceedings. 2006. p. 586-598 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3907 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective floor plan optimisation
AU - Brintrup, Alexandra Melike
AU - Takagi, Hideyuki
AU - Ramsden, Jeremy
PY - 2006/7/14
Y1 - 2006/7/14
N2 - We propose a sequential IGA, multi-objective IGA and parallel interactive genetic algorithm (IGA), and evaluate them with a multi-objective floor planning task through both simulation and real IGA users. Combining human evaluation with an optimization system for engineering design enables us to embed domain specific knowledge which is frequently hard to describe, subjective criteria and preferences in engineering design. We introduce IGA technique to extend previous approaches with sequential single objective GA and multi-objective GA. We also introduce parallel IGA newly. Experimental results show that (1) the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and (2) the multi-objective IGA provides more diverse results and faster convergence for a floor planning task although the parallel IGA provides better fitness convergence.
AB - We propose a sequential IGA, multi-objective IGA and parallel interactive genetic algorithm (IGA), and evaluate them with a multi-objective floor planning task through both simulation and real IGA users. Combining human evaluation with an optimization system for engineering design enables us to embed domain specific knowledge which is frequently hard to describe, subjective criteria and preferences in engineering design. We introduce IGA technique to extend previous approaches with sequential single objective GA and multi-objective GA. We also introduce parallel IGA newly. Experimental results show that (1) the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and (2) the multi-objective IGA provides more diverse results and faster convergence for a floor planning task although the parallel IGA provides better fitness convergence.
UR - http://www.scopus.com/inward/record.url?scp=33745785822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745785822&partnerID=8YFLogxK
U2 - 10.1007/11732242_56
DO - 10.1007/11732242_56
M3 - Conference contribution
AN - SCOPUS:33745785822
SN - 3540332375
SN - 9783540332374
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 586
EP - 598
BT - Applications of Evolutionary Computing - EvoWorkshops 2006
ER -