Publisher's Synopsis
With the current trends in the field of massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Intention of the book is to present recent improvements, innovative ideas and concepts in a part of a huge evolutionary algorithms (EA) field. Evolutionary algorithm is an umbrella term used to describe computer-based problem solving systems which use computational models of evolutionary processes as key elements in their design and implementation. Evolutionary algorithms (EAs) are inspired by the biological model of evolution and natural selection first proposed by Charles Darwin in 1859. In the natural world, evolution helps species adapt to their environments. Environmental factors that influence the survival prospects of an organism include climate, availability of food and the dangers of predators. Species change over the course of many generations. Mutations occur randomly. Some mutations will be advantageous, but many will be useless or detrimental. Progress comes from the feedback provided by non-random natural selection. For example, organisms that can survive for long periods without water will be more likely to thrive in dry conditions than those that can't. Likewise, animals that can run fast will be more successful at evading predators than their slower rivals. If a random genetic modification helps an organism to survive and to reproduce, that modification will itself survive and spread throughout the population, via the organism's offspring. Evolutionary algorithms are based on a simplified model of this biological evolution. To solve a particular problem we create an environment in which potential solutions can evolve. The environment is shaped by the parameters of the problem and encourages the evolution of good solutions. The field of Evolutionary Computation encompasses several types of evolutionary algorithm. These include Genetic Algorithms (GAs), Evolution Strategies, Genetic Programming (GP), Evolutionary Programming and Learning Classifier Systems. The most common type of evolutionary algorithm is the generational genetic algorithm. Evolutionary algorithms are successively applied to wide optimization problems in the engineering, marketing, operations research, and social science, such as include scheduling, genetics, material selection, structural design and so on. Apart from mathematical optimization problems, evolutionary algorithms have also been used as an experimental framework within biological evolution and natural selection in the field of artificial life. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. Evolutionary Algorithms aims to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. This book consists of 14 chapters contributed by renowned experts. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Evolutionary Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Evolutionary Algorithms.
