In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition task. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically. The training consists in coevolving feature extraction procedures, each being a sequence of elementary image processing and feature extraction operations. Extensive experimental results show that the approach attains competitive performance for 3-D object recognition in real synthetic aperture radar (SAR) imagery. 1.

The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.

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This report summarizes the results of research on the use of evolutionary learning for solving pattern recognition problems. The general idea consists in evolutionary search in the space of pattern recognition programs. The whole body of results described here was obtained in the improved version of GPVIS environment [15]. In particular, this report describes the 2.0 version of the environment and is devoted in a great part to the extensions beyond the standard genetic programming introduced into GPVIS, including the novel method of hypothesis evaluation proposed for evolutionary learning. This work focuses on reasoning from pictorial information based on evolutionary computation, or, to be more precise, on the paradigm of genetic programming [12]. The outline of the method is as follows. The genetic search engine performs the search through the space of image processing and analysis programs. The programs have the form of expressions formulated in a specialized language called GPVISL (Genetic Programming for Visual Learning language). The genetic search engine realizes the selection of parent solutions (individuals), which are then crossed over and mutated to obtain the next generation of solutions. The selection is done w.r.t. the value of evaluation (fitness) function. A solution is evaluated by testing its behavior on a set of fitness cases, which are equivalent to images in this context. The fitness function is the percentage of hits[14], i.e. of the correct decisions (recognitions) made by the system. Table of contents 1