A study of genotype and phenotype distributions in hybrid evolutionary algorithms to solve the flow shop scheduling problem

In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are appli...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Minetti, Gabriela F., Alfonso, Hugo, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2002
Materias:
Ciencias Informáticas
Algorithms
Heuristic methods
Scheduling
ARTIFICIAL INTELLIGENCE
flow shop sequencing problem
evolutionary algorithms
heuristics
CDS
NEH
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23006
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
Descripción
Sumario:In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over operator, and two conventional heuristics (CDS and a modified NEH) which are applied either before the evolution begins or when it ends. Here we analyze the genotype and phenotype distribution over the final population of individuals trying to establish the algorithm behavior. Although the original Evolutionary Algorithm was created to provide solutions to the Traveling Salesman Problems (TSP), it can be used for this particular kind of scheduling problem because they share a common chromosome representation.

Ejemplares similares