Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution strategies –A comprehensive introduction
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Evaluating Cellular Automata Models by Evolutionary Multiobjective Calibration
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An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation
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Exploiting spatio–temporal data for the multiobjective optimization of cellular automata models
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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IEEE Transactions on Evolutionary Computation
Parallel simulation of urban dynamics on the GPU
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
Optimizing cellular automata through a meta-model assisted memetic algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Cellular automata simulation of urban dynamics through GPGPU
The Journal of Supercomputing
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We present a comparative study of seven evolutionary algorithms (Generational Genetic, Elitist Genetic, Steady State Genetic, (μ/ρ, λ) Evolution Strategy, (μ/ρ+λ) Evolution Strategy, generational and elitist Covariance Matrix Adaptation) for automatic calibration of a constrained cellular automaton (CCA), whose performance are assessed in terms of two fitness metrics (based on Kappa statistics and Lee-Salee Index). Two variations of the CCA (one with 14 and one 27 parameters) were tested jointly with different number of time steps targeted by the calibration procedures. Besides offering some methodological suggestions for this kind of comparative analysis, the findings provide useful hints on the calibration algorithms to be expected to perform better in the application of cellular automata of sort for the simulation of land-use dynamics.