A hybrid order-based genetic algorithm solution to the classified advertisements layout problem

Pao Yue-kong Library Electronic Theses Database

A hybrid order-based genetic algorithm solution to the classified advertisements layout problem

 

Author: Wong, Chi-fai Ivan
Title: A hybrid order-based genetic algorithm solution to the classified advertisements layout problem
Degree: M.Sc.
Year: 1999
Subject: Advertising layout and typography -- Data processing
Genetic algorithms
Advertising, Classified -- Data processing
Advertising, Newspaper -- Data processing
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: viii, 106 leaves : ill. ; 31 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1460531
URI: http://theses.lib.polyu.edu.hk/handle/200/3090
Abstract: The aim of this dissertation is to describe the hybrid order-based genetic algorithms for solving the classified advertisements layout problem in newspaper. The classified advertisements layout problem is simply to find a layout for the classified advertisements, so that the newspaper pages are fully utilization and the appearance of the newspaper pages is tidy and professional. An existing solution of this problem based on greedy algorithms will be described. After reviewing the related literature, it is found that the problem is a special case of the general "bin-packing" problems. Since the "bin-packing" problems are "NP-complete", the classified advertisements layout problem is also "NP-complete". Therefore, an exact and efficient algorithm for solving the classified advertisements layout problem there should not be exists. Although "bin-packing" problems are "NP-complete", some classical algorithms have been developed and proved to have guaranteed performances. The description of these classical algorithms will also be provided. It was found that one of the classical algorithms, the "First Fit Decreasing" (FFD) is very similar to the existing solution based on greedy algorithm. In order to improve the result provided by the existing algorithms, the existing algorithm is hybridized to genetic algorithms to form a hybrid order-based genetic algorithm for this particular problem. Then the performance of this hybrid order-based genetic algorithm is tested by comparing the performance of this algorithm with the classified algorithms and with the algorithms based on random variables. Since the genetic algorithm and the algorithms for comparison have the probabilistic nature, the comparison is actually a statistic on the performance after a number of independent try runs.

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