New Insights Into Diversification of Hyper-Heuristics

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@Article{Ren:2014:ieeeCybernetics,

• author = "Zhilei Ren and He Jiang and Jifeng Xuan and Yan Hu and Zhongxuan Luo",
• title = "New Insights Into Diversification of Hyper-Heuristics",
• journal = "IEEE Transactions on Cybernetics",
• year = "2014",
• volume = "44",
• number = "10",
• month = oct,
• pages = "1747--1761",
• keywords = "genetic algorithms, genetic programming, Hyper-heuristics, Ising spin glass, instance perturbation, linear genetic programming, p-median",
• DOI = "doi:10.1109/TCYB.2013.2294185",
• ISSN = "2168-2267",
• abstract = "There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realised by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation. In contrast to existing approaches, the proposed mechanism achieves the diversification by perturbing the instance under solving, rather than the solutions. To tackle the challenge of incorporating instance perturbation into hyper-heuristics, we also design a new hyper-heuristic framework HIP-HOP (recursive acronym of HIP-HOP is an instance perturbation-based hyper-heuristic optimisation procedure), which employs a grammar guided high level strategy to manipulate the low level heuristics. With the expressive power of the grammar, the constraints, such as the feasibility of the output solution could be easily satisfied. Numerical results and statistical tests over both the Ising spin glass problem and the p-median problem instances show that HIP-HOP is able to achieve promising performances. Furthermore, run time distribution analysis reveals that, although being relatively slow at the beginning, HIP-HOP is able to achieve competitive solutions once given sufficient time.",
• notes = "Also known as \cite{6690192}",

}

Genetic Programming entries for Zhilei Ren He Jiang Jifeng Xuan Yan Hu Zhongxuan Luo

Citations