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Optimal advertising campaign generation for multiple brands using MOGA

Fleming, P.J. and Pashkevich, M.A. (2007) Optimal advertising campaign generation for multiple brands using MOGA. IEEE Transactions on Systems Man and Cybernetics Part C, 37 (6). pp. 1190-1201. ISSN 1094-6977


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The paper proposes a new modified multiobjective genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using the real data sets from the Canadian advertising industry.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 18 Dec 2007 16:44
Last Modified: 08 Feb 2013 16:55
Published Version: http://dx.doi.org/10.1109/TSMCC.2007.900651
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: 10.1109/TSMCC.2007.900651
URI: http://eprints.whiterose.ac.uk/id/eprint/3540

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