Introspective learning for case-based planning
Introspective learning for case-based planning
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Raising the Dead: Extending Evolutionary Algorithms with a Case-Based Memory
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Improving problem definition through interactive evolutionary computation
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Multiobjective Optimization Using Adaptive Pareto Archived Evolution Strategy
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Case-based introspective learning
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
Environmental Modelling & Software
Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks
Environmental Modelling & Software
Environmental Modelling & Software
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Interactive genetic algorithms with large population and semi-supervised learning
Applied Soft Computing
An IGA-based design support system for realistic and practical fashion designs
Computer-Aided Design
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Interactive optimization algorithms use real-time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm - Case-Based Micro Interactive Genetic Algorithm - that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker's preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.