Visual space perception model identification by evolutionary search

Visual perception of Spaces is relevant for design. Designs, which satisfy perceptual requirements are found based on assessments of perceptual implications. For this purpose a probabilistic model of human visual space perception is used. Focus of this paper is the identification of optimal model parameters, so that the perception model matches the perception of human experimenters. This is accomplished by genetic algorithm, which is an evolutionary optimization method from the domain of computational intelligence, which is able to deal with the probabilistic and discrete nature of the perception model to be identified.