Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem
Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfull...
| Autores principales: | , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2001
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23420 |
| Aporte de: |
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I19-R120-10915-23420 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. |
| spellingShingle |
Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. Vilanova, Gabriela Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| topic_facet |
Ciencias Informáticas Algorithms Scheduling ARTIFICIAL INTELLIGENCE Evolutionary algorithms Multiple Crossovers Multiple Parents Parameter Control. |
| description |
Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multiple crossovers on multiple parents). Evolutionary algorithms have been successfully applied to solve scheduling problems. MCMP-STUD and MCMP-SRI are novel MCMP variants, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents In this paper the proposal is to generate the stud-breeding individual by means of a robust conventional heuristic, the CDS. In a multirecombined EA, setting of parameters n1 (number of crossovers) and n2 (number of parents) remained as an open question. In previous works; they were empirically determined, or a deterministic rule was applied. In this paper self adaptation of parameters n1 and n2 is implemented, the idea is to code the parameters within the chromosome and undergo genetic operations. Hence it is expected that better parameter values be more intensively propagated.
The present paper discusses different multi-recombined methods and contrasts their performance when different parameter control methods are applied, to find the minimum makespan for selected instances of the FSSP. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Vilanova, Gabriela Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector |
| author_facet |
Vilanova, Gabriela Villagra, Andrea Pandolfi, Daniel San Pedro, María Eugenia de Gallard, Raúl Hector |
| author_sort |
Vilanova, Gabriela |
| title |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| title_short |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| title_full |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| title_fullStr |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| title_full_unstemmed |
Parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| title_sort |
parameter control in multirecombinated evolutionary algorithms for the flow shop scheduling problem |
| publishDate |
2001 |
| url |
http://sedici.unlp.edu.ar/handle/10915/23420 |
| work_keys_str_mv |
AT vilanovagabriela parametercontrolinmultirecombinatedevolutionaryalgorithmsfortheflowshopschedulingproblem AT villagraandrea parametercontrolinmultirecombinatedevolutionaryalgorithmsfortheflowshopschedulingproblem AT pandolfidaniel parametercontrolinmultirecombinatedevolutionaryalgorithmsfortheflowshopschedulingproblem AT sanpedromariaeugeniade parametercontrolinmultirecombinatedevolutionaryalgorithmsfortheflowshopschedulingproblem AT gallardraulhector parametercontrolinmultirecombinatedevolutionaryalgorithmsfortheflowshopschedulingproblem |
| bdutipo_str |
Repositorios |
| _version_ |
1764820465901109251 |