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Guided local search is a metaheuristic search method. A meta-heuristic method is a method that sits on top of a local search algorithm to change its behavior.
Guided local search builds up penalties during a search. It uses penalties to help local search algorithms escape from local minima and plateaus. When the given local search algorithm settles in a local optimum, GLS modifies the objective function using a specific scheme (explained below). Then the local search will operate using an augmented objective function, which is designed to bring the search out of the local optimum. The key is in the way that the objective function is modified.
The method in its current form was developed by Dr Christos Voudouris and detailed in his PhD Thesis.[1] GLS was inspired by and extended GENET, a neural network architecture for solving Constraint Satisfaction Problems, which was developed by Chang Wang, Edward Tsang and Andrew Davenport. Both GLS's and GENET's mechanism for escaping from local minima resembles reinforcement learning.
^Voudouris, C, Guided local search for combinatorial optimisation problems, PhD Thesis, Department of Computer Science, University of Essex, Colchester, UK, July, 1997
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