Inserting problem-specific knowledge in multirecombined evolutionary algorithms

In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems,...

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Autores principales: Pandolfi, Daniel, San Pedro, María Eugenia de, Villagra, Andrea, Vilanova, Gabriela, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2002
Materias:
Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22065
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22065
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
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
spellingShingle Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
Inserting problem-specific knowledge in multirecombined evolutionary algorithms
topic_facet Ciencias Informáticas
Inserting problem-specific knowledge
multirecombined evolutionary algorithms
ARTIFICIAL INTELLIGENCE
Knowledge acquisition
Algorithms
description In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find a schedule that minimizes the tardiness penalties. Both problems, even in theirs simplest formulations, are an NP-Hard optimization problem. This presentation discusses how problem specific knowledge is inserted into the evolutionary algorithm to enhance its performance.
format Objeto de conferencia
Objeto de conferencia
author Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author_facet Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author_sort Pandolfi, Daniel
title Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_short Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_full Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_fullStr Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_full_unstemmed Inserting problem-specific knowledge in multirecombined evolutionary algorithms
title_sort inserting problem-specific knowledge in multirecombined evolutionary algorithms
publishDate 2002
url http://sedici.unlp.edu.ar/handle/10915/22065
work_keys_str_mv AT pandolfidaniel insertingproblemspecificknowledgeinmultirecombinedevolutionaryalgorithms
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AT villagraandrea insertingproblemspecificknowledgeinmultirecombinedevolutionaryalgorithms
AT vilanovagabriela insertingproblemspecificknowledgeinmultirecombinedevolutionaryalgorithms
AT gallardraulhector insertingproblemspecificknowledgeinmultirecombinedevolutionaryalgorithms
bdutipo_str Repositorios
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