Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling

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Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good balance between exploration...

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Autores principales: Pandolfi, Daniel, Lasso, Marta Graciela, San Pedro, María Eugenia de, Villagra, Andrea, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2003
Materias:
Ciencias Informáticas
Scheduling
Heuristic methods
ARTIFICIAL INTELLIGENCE
Intelligent agents
Average tardiness scheduling problem
Evolutionary scheduling
conventional heuristics
problem-specific knowledge
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22727
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22727
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
Scheduling
Heuristic methods
ARTIFICIAL INTELLIGENCE
Intelligent agents
Average tardiness scheduling problem
Evolutionary scheduling
conventional heuristics
problem-specific knowledge
spellingShingle Ciencias Informáticas
Scheduling
Heuristic methods
ARTIFICIAL INTELLIGENCE
Intelligent agents
Average tardiness scheduling problem
Evolutionary scheduling
conventional heuristics
problem-specific knowledge
Pandolfi, Daniel
Lasso, Marta Graciela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
topic_facet Ciencias Informáticas
Scheduling
Heuristic methods
ARTIFICIAL INTELLIGENCE
Intelligent agents
Average tardiness scheduling problem
Evolutionary scheduling
conventional heuristics
problem-specific knowledge
description Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good balance between exploration and exploitation. Even though in difficult problems with large search spaces a considerable number of evaluations are required to arrive to near-optimal solutions. On the other hand specialized heuristics are based on some specific features of the problem, and the solution obtained can include some features of optimal solutions. If we insert in the evolutionary algorithm the problem specific knowledge embedded in good solutions (seeds), coming from some other heuristic or from the evolutionary process itself, we can expect that the algorithm will be guided to promising sub-spaces avoiding a large search. This work shows alternative ways to insert knowledge in the search process by means of the inherent information carried by solutions coming from that specialised heuristic or gathered by the evolutionary process itself. To show the efficiency of this approach, the present paper compares the performance of multirecombined evolutionary algorithms with and without knowledge insertion when applied to selected instances of the Average Tardiness Problem in a single machine environment.
format Objeto de conferencia
Objeto de conferencia
author Pandolfi, Daniel
Lasso, Marta Graciela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
author_facet Pandolfi, Daniel
Lasso, Marta Graciela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
author_sort Pandolfi, Daniel
title Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
title_short Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
title_full Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
title_fullStr Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
title_full_unstemmed Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
title_sort knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
publishDate 2003
url http://sedici.unlp.edu.ar/handle/10915/22727
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