Computational Intelligence

Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. This book is the first in a new series entitled "Intelligent Systems Reference Library". It is a collection of chapters written by leading experts, covering a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence (CI). Authors in this collection recognize the limitations of individual paradigms, and propose some practical and novel ways in which different CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful problem-solving environments. Common themes to be found in the various chapters of this collection include the following: Fusion, Collaboration, and Emergence. Fusion describes the hybridization of two or more techniques, at least one of which will involve CI. Collaboration ensures that the different techniques work effectively together. Finally, Emergence refers to the phenomenon that complex behaviour can arise as a result of collaboration between simple processing elements. The book covers a wide range of cutting edge techniques and applications, and is divided into the following parts: I. Introduction II. Fusing evolutionary algorithms and fuzzy logic III. Adaptive solution schemes IV. Multi-agent systems V. Computer vision VI. Communication for CI systems VII. Artificial immune systems VIII. Parallel evolutionary algorithms IX. CI for clustering and classification This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call “intel- gence”fortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e. , systems in which the whole 1 is greater than the sum of the parts . Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more ?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase “Arti?cial Intelligence” (AI), although CI is p- ceived moreas a “bottom up” approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthe“topdown”,andderivefrom pondering upon the “meaning of intelligence”. (These and other key issues will be discussed in more detail in Chapter 1.