Abstract
A computer-based genetic algorithm with an information-based model selection criterion (Akaike’s information criterion [ AIC ]) was used to identify biological traits to classify 4 life style types of 13 representative predaceous phytoseiid mites. As a form of optimization, the genetic algorithm is a computational problem-solving algorithm that mimics Darwinian evolution factors such as fitness, selection, recombination (crossovers), and mutation. The genetic algorithm seeks best solutions to an objective function from many possibilities. Each subset model of traits is treated as a living organism. The AIC is calculated for each model as its fitness value. The genetic algorithm searches for the “best” subset within the landscape of all AIC fitness values to find the best combination. Of 24 provisional traits for 13 species of phytoseiids, 7, including those for developmental rates and 6 individual categories of prey-food proportions were always included in best solutions for the classification of the 4 life style types that previously were identified. These well-studied traits and other less-well studied traits show potential as useful indicators of life style types. Other means to simultaneously optimize selection of life style types and biological traits are discussed.
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