Veröffentlicht: 2005 November
Journal: Journal of Universal Knowledge Management
In this paper we investigate the capabilities of Genetic Algorithms applied to the domain of Knowledge Intensive Process Improvement (Knowiπ). Knowledge intensive processes (KnowiP) can be seen as sequences of activities based on know- ledge intensive acquisition and handling. Such knowledge intensive processes can be implemented in enterprises of different kind regardless of which type, production or service company. In order to measure the performance of knowledge intensive processes, performance indexes are necessary. The processes are evaluated according to these indexes. Two particular Genetic Algorithms (GAs) are applied to improve a special class of knowledge intensive processes. In a single-objective algorithm we aim at improving the duration of the process execution. Moreover, we address the presence of multiple evaluation criteria by a Multi-Objective Genetic Algorithm (MOGA) to find acceptable Pareto solutions as trade-offs. For our case, we investigate the Multi-Sexual GA (MSGA) considering the criteria service time, costs of acquisition and usage of knowledge sources simultaneously.
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