Hyper-heuristic

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem. There might be multiple heuristics from which one can choose for solving a problem, and each heuristic has its own strength and weakness. The idea is to automatically devise algorithms by combining the strength and compensating for the weakness of known heuristics. In a typical hyper-heuristic framework there is a high-level methodology and a set of low-level heuristics (either constructive or perturbative heuristics). Given a problem instance, the high-level method selects which low-level heuristic should be applied at any given time, depending upon the current problem state (or search stage) determined by features.

A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem. There might be multiple heuristics from which one can choose for solving a problem, and each heuristic has its own strength and weakness. The idea is to automatically devise algorithms by combining the strength and compensating for the weakness of known heuristics. In a typical hyper-heuristic framework there is a high-level methodology and a set of low-level heuristics (either constructive or perturbative heuristics). Given a problem instance, the high-level method selects which low-level heuristic should be applied at any given time, depending upon the current problem state (or search stage) determined by features. Modern Hyperheuristics for Large Scale Optimization Problems @ META2012 Tutorial on Hyper-heuristics and Cross-domain Optimization @ GECCO 2012 Self-* Search Track @ GECCO 2012 Special Session on Evolutionary Based Hyperheuristics and Their Applications @ IEEE CEC2012 (WCCI2012) Special Session on Cross-domain Heuristic Search (LION-CHESC) @ LION2012 Cross-domain Heuristic Search Challenge 2011 (CHeSC 2011) Special Session on Systems to Build Systems @ MISTA 2011 Tutorial on Automated Heuristic Design @ GECCO 2011 Special Session on Hybrid Evolutionary Algorithms, Hyper-heuristics and Memetic Computation @ IEEE CEC2010 (WCCI 2010) Workshop on Self-tuning, self-configuring and self-generating search heuristics (Self* 2010) @ PPSN 2010 Workshop on Hyper-heuristics @ PPSN 2008

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