A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic...

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Bibliographic Details
Published in:The Journal of the Operational Research Society 2015-05, Vol.66 (5), p.782-793
Main Authors: Ries, Jana, Beullens, Patrick
Format: Article
Language:English
Subjects:
Algorithms
Automation
Business and Management
Decision trees
Design
Explicit knowledge
Fuzzy logic
fuzzy systems
General Paper
General Papers
Heuristic
heuristics
Management
Operations research
Operations Research/Decision Theory
parameter calibration
Preferences
Studies
Travelling Salesman Problem
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Summary:Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker's preference about the trade-off between computational time and solution quality.
ISSN:0160-5682
1476-9360
DOI:10.1057/jors.2014.46