Managing Knowledge in Unstructured Decisions through Genetic Algorithms for Strategic Information Systems

摘要

Decision making can be classified by organizational level, corresponding to the strategic, management, knowledge, and operational levels. Operational control personnel face fairly well-structured problems. In contrast, strategic planners tackle highly unstructured problems. At this level, knowledge-based core competencies play an important role to handle unstructured problems. Knowledge Management increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes. Evolution of natural language messages was explored in neuro-linguistic programming and studied in the evolutionary theory of language. Genetic algorithms are problem-solving programs that try to mimic the way large populations solve problems over a long period of time, through processes such as reproduction, mutation, and natural selection. Consequently, genetic algorithms promote the evolution of solutions by using genetically based processes. This paper attempts a framework of evolutionary knowledge management in handling unstructured decisions for a Strategic Information Systems based on concepts of Genetic Algorithms.