Krzysztof Krawiec – författare

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 recognition systems. Incorporating "learning" into recognition systems will enable these systems to automatically generate new features on the fly and cleverly 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--such as genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithms--to automate the synthesis and analysis of object detection and recognition systems.

The purpose of incorporating learning into the system design is to avoid the time-consuming process of feature generation and selection and to reduce the cost of building 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.

This book addresses various aspects of electric mobility deployment in public transport. These include transport policy-related issues as well as technical, organizational and technical dimensions of the fleet conversion process (from conventional one towards the increased share of electric vehicles in public transport). In the book, one may find, e.g. the determinants for the successful functioning of electrified transport systems (including charging facilities), models and methods for battery electric bus energy consumption, the analysis regarding the charging strategies (including power-grid) as well as electric vehicle battery issues. As the process of fleet conversion is multi-faceted, the book also contains the issues related to cybersecurity in public transport, autonomous vehicles and hyperloop. The book is dedicated to transport professionals, consulting companies and researchers in the field of electromobility and modern transport systems. 

Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities'' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck'' leaves search algorithm underinformed about the actual and potential qualities of candidate programs.