Computational Intelligence: A Methodological Introduction (Texts in Computer Science)

Computational Intelligence: A Methodological Introduction (Texts in Computer Science)

Language: English

Pages: 492

ISBN: 1447150120

Format: PDF / Kindle (mobi) / ePub


This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

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c 2 does not. A one-point crossover at the point marked by | creates two offspring chromosomes, both of which do not match the schema. With a different cut point, however, offspring  matches the schema: Obviously, whether an offspring chromosome matches the schema can depend crucially on the location of the cut point relative to the fixed characters of the schema. This gives rise to the notion of the defining length of a schema: Definition 13.4 (Defining Length of a Schema) The defining

of operators, because any Boolean function with any number of arguments can be represented by appropriate combinations of the operators in these sets. However, is not a complete set, because even the simple negation of an argument cannot be represented. Finding the smallest complete set of operators for a given set of functions to represent is (usually) NP-hard. As a consequence, usually contains more functions than are actually necessary. However, this is not necessarily a disadvantage, since

attention. Only when Rumelhart et al. (1986a, 1986b) independently developed the method again and advertised it in the research community, the modern age (“second bloom”) of (artificial) neural network began, which last to the present day. We consider error backpropagation only in Chap. 5, since it cannot be applied directly to threshold logic units. It requires that the activation of a neuron does not jump at a crisply defined threshold from 0 to 1, but that the activation rises slowly,

multi-layer perceptron. Note that this theorem only requires that the function to represent is Riemann-integrable. It need not be continuous. That is, the function to represent may have “jumps.” However it may have only finitely many “jumps” of finite height in the region in which it is to be approximated by a multi-layer perceptron. In other words, the function must be continuous “almost everywhere.” Note also that in this theorem the approximation error is measured by the area between the

has been found so far. Thus, a possible degradation is always seen in relation to the best found individual. Only if there is an improvement, then the current individual is important. Similar to threshold accepting, a monotonously increasing sequence θ of real numbers controls the selection of poor individuals. More formally: Algorithm 11.9 (Record-to-Record Travel) 1.Choose a (random) starting point s 0∈Ω and set s best=s 0. 2.Choose a point s∈Ω “in the vicinity” of s t (for example, by a

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