Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
A new kind of science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modeling Urban Growth by Cellular Automata
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
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We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance.