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- W983390668 abstract "Genetic algorithms represent a global optimisation method, imitating the principles ofnatural evolution: selection and survival of the fittest. Genetic algorithms operate on arandomly initialised population of potential solutions to a problem. The solutionsdevelop by passing valuable genetic information to succeeding generations.Genetic algorithms are known as a robust technique suitable for a variety ofoptimisation problems. However, when applied to complex combinatorial problemswith multiple parameters, conventional genetic algorithms are usually slow andineffective due to the large search space.This thesis proposes a novel approach to the development of a genetic algorithm andapplies this approach to a maintenance scheduling problem in a power generationsystem. Problem specific knowledge is utilised to divide the problem into several layers,with each layer representing a part of the initial problem. Solutions are progressivelydeveloped, with each layer algorithm finding partial solutions that satisfy specifiedcriteria. These partial solutions are then used as building blocks in the next layer, toprogressively build up complete solutions.The resulting multi-layered genetic algorithm is able to concentrate its search efforts inareas where good quality solutions are likely to be present, therefore producing betterresults than traditional genetic algorithms. Further developments of the multi-layeredgenetic algorithm are also suggested in this thesis. The algorithm is combined with alocal search method, and heuristic rules are used for initialisation of the population. Thecombined method results in an effective and fast exploration of the problem's searchspace and is suitable for a variety of optimisation problems.The proposed algorithm is implemented using MATLAB programming language andtested on a real power generation system. A number of implementation issues, such as specific chromosome structure and a varying generation gap; interchangeable solutionsand gene convergence; weeding out duplicates from the population and reducing thesearch space without losing the quality of representing the problem domain, are alldiscussed. Specifics of a local search method and its representation are also examined.Special attention is paid to developing efficient evaluation and neighbourhoodexploration procedures." @default.
- W983390668 created "2016-06-24" @default.
- W983390668 creator A5041092474 @default.
- W983390668 date "2003-01-01" @default.
- W983390668 modified "2023-09-24" @default.
- W983390668 title "Development and applications of multi-layered genetic algorithms to multi-dimensional optimisation problems" @default.
- W983390668 hasPublicationYear "2003" @default.
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