C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Tabu Neural Network

Tabu search, popularized by Fred Glover with his contributions, is a paradigm that has been used successfully

in many optimization problems. It is a method that can steer a search procedure from a limited domain to an

extended domain, so as to seek a solution that is better than a local minimum or a local maximum.

Tabu search (TS), suggests that an adaptive memory and a responsive exploration need to be part of an

algorithm. Responsive exploration exploits the information derivable from a selected strategy. Such

information may be more substantial, even if the selected strategy is in some sense a bad strategy, than what

you can get even in a good strategy that is based on randomness. It is because there is an opportunity provided

by such information to intelligently modify the strategy. You can get some clues as to how you can modify

the strategy.

When you have a paradigm that incorporates adaptive memory, you see the relevance of associating a neural

network:. a TANN is a Tabu neural network. Tabu search and Kohonen’s self−organizing map have a

common approach in that they work with “neighborhoods.” As a new neighborhood is determined, TS

prohibits some of the earlier solutions, as it classifies them as tabu. Such solutions contain attributes that are

identified as tabu active.

Tabu search, has STM and LTM components as well. The short−term memory is sometimes called

recency−based memory. While this may prove adequate to find good solutions, the inclusion of long−term

memory makes the search method much more potent. It also does not necessitate longer runs of the search

process.

Some of the examples of applications using Tabu search are: