T. Kowaliw and W. Banzhaf and R. Doursat
to appear at ACM GECCO 2013
paper preprint (PDF)
© ACM, (2013). This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in GECCO-2013.
Definitive version & presentation to follow.
We propose an evolutionary feature creator (EFC) to explore a non-linear and offline method for generating features in image recognition tasks. Our model aims at extracting low-level features automatically when provided with an arbitrary image database. In this work, we are concerned with the addition of algorithmic depth to a genetic programming (GP) system, hypothesizing that it will improve the capacity for solving problems that require high-level, hierarchical reasoning. For this we introduce a network superstructure that co-evolves with our low-level GP representations. Two approaches are described: the first uses our previously used ``shallow'' GP system, the second presents a new ``deep'' GP system that involves this network superstructure. We evaluate these models against a benchmark object recognition database. Results show that the deep structure outperforms the shallow one in generating features that support classification, and does so without requiring significant additional computational time. Further, high accuracy is achieved on the standard ETH-80 classification task, also outperforming many existing specialized techniques. We conclude that our EFC is capable of data-driven extraction of useful features from an object recognition database.