Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Knowledge Management for the Information Professional
Knowledge Management for the Information Professional
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
DISSIMILAR SETS OF EXPERIENCE KNOWLEDGE STRUCTURE: A NEGOTIATION PROCESS FOR DECISIONAL DNA
Cybernetics and Systems
Using XML for implementing set of experience knowledge structure
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Engineering Applications of Artificial Intelligence
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Set of Experience Knowledge Structure (SOE) has been shown as a tool able to collect and manage explicit knowledge of formal decision events. This structure, after being homogenized and mixed, offers a set of possible solutions that, probably, could be improved. The purpose of this article is to show a search process for improved optimal solutions by implementing Evolutionary Algorithms-EA (Genetic Algorithms-GA). Afterward, according to the user's priorities, a unique optimal solution is chosen. Subsequently, such holistic improved SOE is stored as an experienced decision, feeding a knowledge repository of Decisional DNA that would be a useful technology within many different intelligent systems and platforms, including the Knowledge Supply Chain System (KSCS).