Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Evolvable Machines
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the flight period of the pest and for optimizing the layout of dispensers in the treated area. A new experimental dispenser has been recently designed by researchers at the Instituto Agroforestal del Mediterraneo - Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling of the release kinetics of this dispensers is the difficulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second objective measures how "smooth" the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.