A methodology for determining an effective subset of heuristics in selection hyper-heuristics

Abstract

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic usingmulti-armed banditscoupled with a change detection mechanism. The methodology is tested on two real-world optimisation problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances

A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics

“…Hyper-Heuristic. In order to test the performance of the previously defined intensification and diversification groups of heuristics, we follow the experimental setup in [10] with some adjustments detailed in this section. We also provide a description of the high-level strategy and adaptive operator selection mechanism used.…”

“…We utilize iterated local search (ILS) as the high-level strategy [8,10,16]. Iterated local search is a simple yet effective strategy, which works by iteratively alternating between an exploration move (diversification) and an exploitation move (intensification) from the perturbed solution [7].…”

“…Several variants of iterated local search hyper-heuristics have been proposed with good results [8,9]. Soria-Alcaraz et al [10] proposed a methodology for determining the best subset of heuristics from a given pool, but they considered that all operators were intensification heuristics and the diversification stage contained a single fixed heuristic.…”

“…Based on this probing tool and statistical techniques, a set of measurements are collected indicating the effectiveness of each operator to either diversify or intensify the search. e proposed methodology is tested within a state-ofthe-art iterated local search hyper-heuristic [10] on two problem domains with different representations, namely, course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem.…”

Application of Population Evolvability in a Hyper-Heuristic for Dynamic Multi-Objective Optimization

“…There are few works that use fitness landscape analysis methods as a part of a hyper-heuristic. In Soria-Alcaraz, Ochoa, Sotelo-Figeroa, and Burke (2017), evolvability and landmarking are used by considering a ratio of fitness-improving solutions from a set of neighbours of an initial solution and by using a First-Improvement Hill-Climbing of a solution, re-spectively. Both methods are used as part of offline-learning strategies to define selection probabilities to each low-level heuristic.…”