This book describes several generic algorithmic concepts that can be used in any kind of GA or with evolutionary optimization techniques. It provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, the authors show how to substantially increase achievable solution quality. They also describe structure identification using HeuristicLab as a platform for algorithm development. Software, dynamical presentations of representative test runs, and more are available on a supplementary website.
