Ant based Hyper Heuristic for Physical Impairment aware routing and wavelength assignment

Keleş, Ali; Yayimli, Aysegul; Uyar, A. Şima

doi:10.1109/sarnof.2010.5469778

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Some interesting alternatives to GP to construct programs include the use of a variation of Estimation of Distribution Algorithms called Estimation of Distribution Programming [104] and a variation of Ant Colony Optimisation called Ant Programming [99,100]. Although ant algorithms have been used as a hyper-heuristic to select heuristics [21,29,33,62], to the best of our knowledge neither technique has been used as a hyper-heuristic to generate heuristics, and this could be an interesting research direction.…”

Section: Algorithm Spacementioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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