Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and pattern recognition systems. Incorporating learning into recognition systems will enable these systems to automatically select a good subset of features according to the type of objects and images to which they are applied. This unique monograph investigates evolutionary computational techniques---genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithms---to automate the synthesis and analysis of object detection and recognition systems. Researchers, professionals, engineers, and students working in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology will find this well-developed and organized volume an invaluable resource.

Integrates computer vision, pattern recognition, and AI.

Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology