Evolutionary algorithms (EA) have received considerable attention regarding their potential for solving real-world problems involving various types of tasks, such as optimization of objectives, satisfaction of constraints or modeling data. Despite of the diversity of tasks they can handle, evolutionary algorithms form one family of problem solving techniques, based on the principle of"survival of the fittest", mimicing some natural phenomena of genetic and phenotypic inheritance and Darwinian strife for survival. They also constitute an interesting category of modern heuristic search. They form a class of adaptive algorithms with operations based on probabilistic procedures for creating and maintaining individuals in a population of candidate solutions, and through a process of variation and a selection, findingnear-optimum solution.
Evolutionary techniques have been successfully applied to a variety of difficult problems. These include numerical and combinatorial optimization, machine learning, optimal control, cognitive modeling, classical operation research problems (m travelling salesman problem, knapsack problems, transportation problems, assignment problems , bin packing, scheduling, partitioning, etc.), engineering design, system integration, iterated games, robotics, signal processing, and many others.
The editors of this book are proud to present a collection of papers that reflects the diversity of the field of evolutionary computing.