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Abstract

Genetic algorithms represent a global optimisation method, imitating the principles of
natural evolution: selection and survival of the fittest. Genetic algorithms operate on a
randomly initialised population of potential solutions to a problem. The solutions
develop by passing valuable genetic information to succeeding generations.
Genetic algorithms are known as a robust technique suitable for a variety of
optimisation problems. However, when applied to complex combinatorial problems
with multiple parameters, conventional genetic algorithms are usually slow and
ineffective due to the large search space.
This thesis proposes a novel approach to the development of a genetic algorithm and
applies this approach to a maintenance scheduling problem in a power generation
system. Problem specific knowledge is utilised to divide the problem into several layers,
with each layer representing a part of the initial problem. Solutions are progressively
developed, with each layer algorithm finding partial solutions that satisfy specified
criteria. These partial solutions are then used as building blocks in the next layer, to
progressively build up complete solutions.
The resulting multi-layered genetic algorithm is able to concentrate its search efforts in
areas where good quality solutions are likely to be present, therefore producing better
results than traditional genetic algorithms. Further developments of the multi-layered
genetic algorithm are also suggested in this thesis. The algorithm is combined with a
local search method, and heuristic rules are used for initialisation of the population. The
combined method results in an effective and fast exploration of the problem's search
space and is suitable for a variety of optimisation problems.
The proposed algorithm is implemented using MATLAB programming language and
tested on a real power generation system. A number of implementation issues, such as specific chromosome structure and a varying generation gap; interchangeable solutions
and gene convergence; weeding out duplicates from the population and reducing the
search space without losing the quality of representing the problem domain, are all
discussed. Specifics of a local search method and its representation are also examined.
Special attention is paid to developing efficient evaluation and neighbourhood
exploration procedures.

Item Type:	Thesis - PhD
Authors/Creators:	Kelareva, Galina Vladislavovna
Keywords:	Combinatorial optimization, Genetics
Copyright Holders:	The Author
Copyright Information:	Copyright 2003 the Author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s).
Additional Information:	Thesis (Ph.D.)--University of Tasmania, 2003. Includes bibliographical references
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