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Leveraging saving-based algorithms by master-slave genetic algorithms

Leveraging saving-based algorithms by master-slave genetic algorithms
Leveraging saving-based algorithms by master-slave genetic algorithms
Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms
0952-1976
555-566
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Benedettini, Stefano
037b9551-1edc-4a9d-97e7-9f8a261019dd
Roli, Andrea
d91f00c6-6d2f-4d2e-ae8f-00dde2eb7f97
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Benedettini, Stefano
037b9551-1edc-4a9d-97e7-9f8a261019dd
Roli, Andrea
d91f00c6-6d2f-4d2e-ae8f-00dde2eb7f97

Battarra, Maria, Benedettini, Stefano and Roli, Andrea (2011) Leveraging saving-based algorithms by master-slave genetic algorithms. Engineering Applications of Artificial Intelligence, 24 (4), 555-566. (doi:10.1016/j.engappai.2011.01.007).

Record type: Article

Abstract

Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms

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More information

e-pub ahead of print date: 4 March 2011
Published date: June 2011
Organisations: Operational Research

Identifiers

Local EPrints ID: 204847
URI: https://eprints.soton.ac.uk/id/eprint/204847
ISSN: 0952-1976
PURE UUID: 96d80750-acdd-4219-afd7-1eb5184d450f

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Date deposited: 02 Dec 2011 11:36
Last modified: 16 Jul 2019 23:17

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Contributors

Author: Maria Battarra
Author: Stefano Benedettini
Author: Andrea Roli

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