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 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.