Hyper-heuristics: a survey of the state of the art

Abstract

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

Generalizing Hyper-heuristics via Apprenticeship Learning

“…Both selection and generation hyper-heuristics can be further categorized into construction or perturbation heuristics. More on hyper-heuristics can be found in [3,13,16]. Selection hyperheuristics have been well studied, and gave rise to the CHeSC 2011 competition 3 ; further details of this can be found in [7,12] and at the CHeSC website, and of the winning hyper-heuristic by Misir et al in [11].…”

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

“…In this work we employ an algorithm based on a hidden Markov model [14], which is described later. A recent survey of hyper-heuristic approaches is provided in [2].…”

Haiku - a Scala Combinator Toolkit for Semi-automated Composition of Metaheuristics

“…As a concrete example of 'composition by hand', previous work [6] has applied Tabu search [14] and simulated annealing [16] at different points in a multi-stage local search algorithm. The desire for greater automation has led to approaches such as hyper-heuristics [3], which are the application of search to the problem of finding good heuristics ('heuristics for searching the space of heuristics'). Of particular interest for the automated design of algorithms are generative hyper-heuristics [5], which assemble basic components into more complex search algorithms.…”