Evolutionary Computation and Experimental Design

  • Meinwen Pryde

    Student thesis: Doctoral Thesis


    This thesis describes the investigations undertaken to produce a novel hybrid optimisation technique that combines both global and local searching to produce good solutions quickly. Many evolutionary computation and experimental design methods are considered before genetic algorithms and evolutionary operation are combined to produce novel optimisation algorithms. A novel piece of software is created to run two and three factor evolutionary operation experiments. A range of new hybrid small population genetic algorithms are created that contain evolutionary operation in all generations (static hybrids) or contain evolutionary operation in a controlled number of generations (dynamic hybrids). A large number of empirical tests are carried out to determine the influence of operators and the performance of the hybrids over a range of standard test functions. For very small populations, twenty or less individuals, stochastic universal sampling is demonstrated to be the most suitable method of selection. The performance of very small population evolutionary operation hybrid genetic algorithms is shown to improve with larger generation gaps on simple functions and on more complex functions increasing the generation gap does not deteriorate performance. As a result of the testing carried out for this study a generation gap of 0.7 is recommended as a starting point for empirical searches using small population genetic algorithms and their hybrids. Due to the changing presence of evolutionary operation, the generation gap has less influence on dynamic hybrids compared to the static hybrids. The evolutionary operation, local search element is shown to positively influence the performance of the small population genetic algorithm search. The evolutionary operation element in the hybrid genetic algorithm gives the greatest improvement in performance when present in the middle generations or with a progressively greater presence. A recommendation for the information required to be reported for benchmarking genetic algorithm performance is also presented. This includes processor, platform, software information as well as genetic algorithm parameters such as population size, number of generations, crossover method and selection operators and results of testing on a set of standard test functions.
    Date of AwardJul 2001
    Original languageEnglish
    SupervisorHefin Rowlands (Supervisor)


    • Evolutionary computation
    • evolutionary operation
    • optimisation algorithms
    • genetic algorithms

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