Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Bayesian network structure learning from limited datasets through graph evolution
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (color, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of french Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modeling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.