# 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.

i=1,…,m. Furthermore, let arbitrary initial weights w and an arbitrary initial threshold θ be given (e.g., chosen randomly). We consider online training first: Algorithm 3.2 (Online Training of a Threshold Logic Unit) Obviously, this algorithm repeatedly applies the delta rule until the sum of errors over all training examples vanishes. Note that in this algorithm the weight adaptation is written in vector form, which, however, is clearly equivalent to an adaptation of the individual weights.

could also compute the firing strength of a rule and the influence of this degree to the fuzzy set in the consequent of this rule based on different t-norms. Some approaches even choose an individual t-norm for each rule. Sometimes, even t-conorms are used to compute the firing strength of a rule. In this case, the corresponding rule must read as In the sense of our interpretation of the rules as a piecewise definition of a fuzzy function, this rule can be replaced by the following n rules.

minimized by assigning each data point crisply to the closest cluster, even though we allowed for degrees of membership. 20.5 Fuzzification by Membership Transformation Since we cannot obtain degrees of membership by merely expanding the range of values of the u ij , we have to modify the objective function if we desire graded assignments. The most common approach is to apply a transformation to the membership degrees, that is, to use an objective function of the form where h is a convex

either), they usually offer good chances of finding a “sufficiently good” solution. The success and the execution time of metaheuristics depend critically on a proper mapping of the problem to the steps of the metaheuristic and the efficient implementation of every single step. Many metaheuristics work by iteratively improving a set of so-called candidate solutions. They differ in the methods they employ to vary solutions in order to possibly improve them, in the principles by which partial

concrete example: the fact that bit triplets are interpreted together, each indicating a file (column) in which a queen is placed.) That is, we completely separate the encoding from the genetic mechanisms, while in an evolutionary algorithm certain aspects of the encoding are considered, for instance, to restrict the genetic operators. (In the above example: in the evolutionary algorithm for the 8-queens problem, we allow as cut points for a crossover operator only the points between bit