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

By Dr James M Whitacre


Although researchers often discuss the rising popularity of meta-heuristics (MH), there has been a paucity of data to directly 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 indeed appears to have surpassed DM as the algorithm framework of choice for solving optimization problems. Motivated by these findings, this paper aims to review and discuss the origins of meta-heuristic dominance.\ud 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 and comprehensive explanation would directly account for the manner in which most MH success has actually been achieved, e.g. through hybridization and customization to a particular problem environment. \ud This paper puts forth the hypothesis that MH derive much of their utility from being flexible. This flexibility is empirically supported by evidence that MH design can adapt to a problem environment and can integrate domain knowledge. 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. We also discuss current trends in optimization problems and speculate that highly flexible algorithm frameworks will become increasingly popular within our diverse and rapidly changing world

Topics: Complexity Theory, Artificial Intelligence
Year: 2009
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