A Note on Open Problems and Challenges in Optimization Theory and Algorithms

Abstract

Hyper-heuristics comprise a set of approaches with the common goal of automating the design and tuning of heuristic methods to solve hard computational search problems. Themain goal is to produce more generally applicable search method- ologies. The term hyper-heuristic was coined in the early 2000s to refer to the idea of heuristics to choose heuristics. However, the idea of automating the design of com- bined heuristics can be traced back to the early 1960s. With the incorporation of Genetic Programming into hyper-heuristic research, a new type of hyper-heuristics has emerged that we have termed heuristics to generate heuristics. The distinguishing fea- ture 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, as is the case with most meta-heuristic approaches. This paper presents a literature survey of hyper-heuristics including their origin and intellectual roots, a de- tailed 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.