Survival of the flexible: explaining the dominance of meta-heuristics within a rapidly evolving world

Although researchers often discuss the popularity of meta-heuristics (MH), there has been a paucity of data to support the notion that MH are growing in prominence compared to deterministic methods (DM). Here we provide the first evidence that MH usage is not only growing, but appears to have surpassed DM as the algorithm framework of choice for solving optimization problems. Explanations for meta-heuristic success are varied, however their robustness to variations in fitness landscape properties is often cited as an important advantage. In this paper, we review explanations for MH popularity and discuss why some of these arguments remain unsatisfying. We argue that a more compelling explanation would directly account for the manner in which most MH success has been achieved, e.g. through customization to a particular problem environment. This paper puts forth the hypothesis that MH derive much of their utility from being flexible. We propose what flexibility means from a search algorithm design context and we propose key attributes that should exist in a flexible algorithm framework. Interestingly, a number of these qualities are observed in robust biological systems. In light of these similarities, we consider whether the origins of biological robustness, (e.g. loose coupling, modularity, partial redundancy) could help to inspire the development of more flexible algorithm frameworks.