Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Selective Sampling Based on the Variation in Label Assignments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
'Managed challenge' alleviates disengagement in co-evolutionary system identification
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
New methods for competitive coevolution
Evolutionary Computation
Resource sharing and coevolution in evolving cellular automata
IEEE Transactions on Evolutionary Computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
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Reservoir modeling is an on-going activity during the production life of a reservoir. One challenge to constructing accurate reservoir models is the time required to carry out a large number of computer simulations. This research investigates a competitive co-evolutionary algorithm to select a small number of informative reservoir samples to carry out computer simulation. The simulation results are also used to co-evolve the computer simulator proxies. We have developed a co-evolutionary system incorporating various techniques to conduct a case study. Although the system was able to select a very small number of reservoir samples to run the computer simulations and use the simulation data to construct simulator proxies with high accuracy, these proxy models do not generalize very well on a larger set of simulation data generated from our previous study. Nevertheless, we have identified that including a test-bank in the system helped mitigating the situation. We will conduct more systematic analysis of the competitive co-evolutionary dynamics to improve the system performance.