Neural network for female mate preference, trained by a genetic algorithm

Abstract

In some animals, males evolve exaggerated traits (e.g. the peacock's conspicuous tail and display) because of female preference. Recently Enquist and Arak presented a simple neural network model for a visual system in female birds that acquires the ability to discriminate males of the correct species from those of the wrong species by training. They reported that the trained networks were attracted by ‘supernormal stimuli’ where there was a greater response to an exaggerated form than to the images used as the correct species for training. They suggested that signal recognition mechanisms have an inevitable bias in response, which in turn causes selection on signal form. We here examine the Enquist and Arak model in detail. A three-layered neural network is used to represent the female's mate preference, which consists of 6 by 6 receptor cells arranged on a regular square lattice, ten hidden cells, and one output cell. Connection weights of the network were modified by a genetic algorithm, in which the female's fitness increases if she accepts a conspecific male but decreases if she accepts a male of a different species or a random image. We found that: (i) after the training period the evolved network was able to discriminate male images. Female preference evolves to favour unfamiliar patterns if they are similar to the images of the correct species (generalization); (ii) the speed and the final degree of learning depended critically on the choice of the random images that are rejected. The learning was much less successful if the random images were changed every generation than if 20 random images were fixed throughout the training period; (iii) the male of the same species used for training achieved the highest probability of being accepted by the trained network. Hence, contrary to Enquist and Arak, the evolved network was not attracted by supernormal stimuli.