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. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond.

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. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond.

The book constitutes the refereed proceedings of the 17th European Conference on Genetic Programming, Euro GP 2014, held in Grenada, Spain, in April 2014 co-located with the Evo*2014 events, Evo BIO, Evo COP, Evo MUSART and Evo Applications. The 15 revised full papers presented together with 5 poster papers were carefully reviewed and selected form 40 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics as diverse as search-based software engineering, image analysis, dynamical systems, evolutionary robotics and operational research to the foundations of search as characterized through semantic variation operators.

This book constitutes the refereed proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, held in Vienna, Austria, in April 2013 co-located with the Evo* 2013 events, EvoMUSART, EvoCOP, EvoBIO, and EvoApplications.
The 18 revised full papers presented together with 5 poster papers were carefully reviewed and selected from 47 submissions. The wide range of topics in this volume reflects the current state of research in the field, including different genres of GP (tree-based, linear, grammar-based, Cartesian), theory, novel operators, and applications.

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

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.

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.

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.