BibTeX
@MISC{Glover_tabusearch,
author = {Fred Glover and Rafael Marti},
title = { TABU SEARCH},
year = {}
}
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Abstract
Tabu Search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Memory based strategies are therefore the hallmark of tabu search approaches, founded on a quest for "integrating principles, " by which alternative forms of memory are appropriately combined with effective strategies for exploiting them. In this chapter we address the problem of training multilayer feed-forward neural networks. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is backpropagation, other optimization methods such as tabu search have been applied to solve this problem. This chapter describes two training algorithms based on the tabu search. The experimentation shows that the procedures provide high quality solutions to the training problem, and in addition consume a reasonable computational effort.
Keyphrases
tabu search many different area multilayer feed-forward neural network local optimality flexible search behavior high quality solution integrating principle optimization method adaptive memory programming effective strategy intelligent problem solving adaptive memory alternative form popular method memory structure reasonable computational effort training problem solution space local heuristic search procedure key word main component tabu search approach training algorithm
