The importance of the learning conditions in hyper-heuristics

  • Authors:

  • Nuno Lourenço;Francisco Baptista Pereira;Ernesto Costa

  • Affiliations:

  • CISUC, Department of Informatics Engineering University of Coimbra, Coimbra, Portugal;CISUC, Department of Informatics Engineering University of Coimbra, Coimbra, Portugal;CISUC, Department of Informatics Engineering University of Coimbra, Coimbra, Portugal

  • Venue:
  • Proceedings of the 15th annual conference on Genetic and evolutionary computation
  • Year:
  • 2013

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Abstract

Evolutionary Algorithms are problem solvers inspired by nature. The effectiveness of these methods on a specific task usually depends on a non trivial manual crafting of their main components and settings. Hyper-Heuristics is a recent area of research that aims to overcome this limitation by advocating the automation of the optimization algorithm design task. In this paper, we describe a Grammatical Evolution framework to automatically design evolutionary algorithms to solve the knapsack problem. We focus our attention on the evaluation of solutions that are iteratively generated by the Hyper-Heuristic. When learning optimization strategies, the hyper-method must evaluate promising candidates by executing them. However, running an evolutionary algorithm is an expensive task and the computational budget assigned to the evaluation of solutions must be limited. We present a detailed study that analyses the effect of the learning conditions on the optimization strategies evolved by the Hyper-Heuristic framework. Results show that the computational budget allocation impacts the structure and quality of the learned architectures. We also present experimental results showing that the best learned strategies are competitive with state-of-the-art hand designed algorithms in unseen instances of the knapsack problem.