Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
ACM Computing Surveys (CSUR)
Genetic algorithms and their statistical applications: an introduction
Computational Statistics & Data Analysis
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Scalable Parallel Genetic Algorithms
Artificial Intelligence Review
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Short Review of Statistical Learning Theory
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Short communication: A generic tool for optimising land-use patterns and landscape structures
Environmental Modelling & Software
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Using neural networks and cellular automata for modelling intra-urban land-use dynamics
International Journal of Geographical Information Science
Assessing a predictive model of land change using uncertain data
Environmental Modelling & Software
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
Environmental Modelling & Software
Environmental Modelling & Software
Inductive pattern-based land use/cover change models: A comparison of four software packages
Environmental Modelling & Software
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Spatially explicit land-use models simulate the patterns of change on the landscape in response to coupled human-ecological dynamics. As these models become more complex involving larger than ever data sets, the need to improve calibration techniques as well as methods that test model accuracy also increases. To this end, we developed a Genetic Algorithm tool and applied it to optimize probability maps of deforestation generated from the Weights of Evidence method for 12 case-study sites in the Brazilian Amazon. We show that the Genetic Algorithm tool, after being constrained during the reproduction process within a specified range and trend of variation of the Weights of Evidence coefficients, was able to overcome overfitting and improve validation fitness scores with acceptable computational costs. In addition to modeling deforestation, the Genetic Algorithm tool coupled with the Weights of Evidence method is flexible enough to embrace a variety of models as well as their specific fitness functions, thus offering a practical way to optimize the performance of land-use change models.