Artificial evolutionary systems are computer systems, inspired by ideas from natural evolution and related phenomena. The field has a long history, dating back to the earliest days of computer science, but it has only become an established scientific and engineering discipline since the 1990s, with packages for the commonest form, genetic algorithms, now widely available.

highly unique and relevant book illustrating the breadth of research in intelligent and evolutionary systems, with applications ranging from business to medicine, from network optimization to the promotion of innovation.

Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination.- Mining Bayesian Networks from Direct Marketing Databases with Missing Values.- Fuzzy Local Currency Based on Social Network Analysis for Promoting Community Businesses.- Evolving Failure Resilience in Scale-Free Networks.- Evolving Networks with Enhanced Linear Stability Properties.- Effectiveness of Close-Loop Congestion Controls for DDoS Attacks.- Priority-Based Genetic Algorithm for Shortest Path Routing Problem in OSPF.- Evolutionary Network Design by Multiobjective Hybrid Genetic Algorithm.- Hybrid Genetic Algorithm for Designing Logistics Network, VRP and AGV Problems.- Multiobjective Genetic Algorithm for Bicriteria Network Design Problems.- Use of Serendipity Power for Discoveries and Inventions.- Evolution of Retinal Blood Vessel Segmentation Methodology Using Wavelet Transforms for Assessment of Diabetic Retinopathy.- Multistage-Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem.- Implementation of Parallel Genetic Algorithms on Graphics Processing Units.