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Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski W., Krawiec K., Wieloch B.

International Journal of Applied Mathematics and Computer Science

2014

Vol. 24, no. 1

183--197

We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.

Genetic programming for primitive-based acquisition of visual concepts

Krawiec K.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2006

z. 156

211-222

We describe a novel method for acquisition of higher-level visual concepts using GP-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: individuals-learners are rewarded for being able to restore the essential features (here: shape) of the visual stimulus. The approach is general and does not require any a priori knowledge about the particular application or target concept to be learned; the only prerequisite is universal knowledge related to interpretation of visual information, encoded in nodes of GP trees. The paper demonstrates the performance of the method on a specific visual task of acquiring the concept of a triangle from examples given in a form of raw raster images.

Evolutionary computation framework for learning from visual examples

Krawiec K.

Image Processing & Communications

2001

Vol. 7, no 3-4

85-96

This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the "standard" genetic programming (as defined by Koza [13] and the genetic programming extended by local optimization of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.