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Undoubtedly, the hardware technology of personal computers (PCs) are continuously changing and shaping our daily living. However, given the diversity of PC hardware components, and the limited compatibility between some of these hardware components, most people are interested to obtain an (sub-)optimal configuration for some specific usage restricted to their budget limits and other possible criteria. In this paper, we firstly formulate these widely occurred configuration problems as discrete optimization problems in which users can flexibly add in or modify their specific requirements at any time. More interestingly, we proposed two intelligent optimizers: a simple-yet-powerful beam search method and a min-conflict heuristic-based micro-genetic algorithm (MGA) to solve this real-life optimization problem. The beam search represents a restricted intelligent search while the MGA is an evolutionary algorithm with small population size. To investigate the feasibility and efficiency of our proposals, we built a Webbased Personal Computer Configuration Advisor to integrate each of the two optimizers as individual component to configure PCs for general users. In our empirical evaluation, the heuristic-based MGA consistently outperformed the beam search method in most cases. Furthermore, our work opens up numerous exciting directions for future investigation including the improvement of our optimizer to handle more complicated users' requirements, the integration of other optimizers like the branch-and-bound heuristic search method [1,2] for comparison, and the possible uses of efficient learning algorithms such as the ID3 algorithm [2] to classify different user-defined configurations into useful examples to guide the search during optimization.
Undoubtedly, the hardware technology of personal computers (PCs) are continuously changing and shaping our daily living. However, given the diversity of PC hardware components, and the limited compatibility between some of these hardware components, most people are interested to obtain an (sub-)optimal configuration for some specific usage restricted to their budget limits and other possible criteria. In this paper, we firstly formulate these widely occurred configuration problems as discrete optimization problems in which users can flexibly add in or modify their specific requirements at any time. More interestingly, we proposed two intelligent optimizers: a simple-yet-powerful beam search method and a min-conflict heuristic-based micro-genetic algorithm (MGA) to solve this real-life optimization problem. The beam search represents a restricted intelligent search while the MGA is an evolutionary algorithm with small population size. To investigate the feasibility and efficiency of our proposals, we built a Webbased Personal Computer Configuration Advisor to integrate each of the two optimizers as individual component to configure PCs for general users. In our empirical evaluation, the heuristic-based MGA consistently outperformed the beam search method in most cases. Furthermore, our work opens up numerous exciting directions for future investigation including the improvement of our optimizer to handle more complicated users' requirements, the integration of other optimizers like the branch-and-bound heuristic search method [1,2] for comparison, and the possible uses of efficient learning algorithms such as the ID3 algorithm [2] to classify different user-defined configurations into useful examples to guide the search during optimization.
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