The 16 revised full papers presented together with 4 invited papers were carefully reviewed and selected for inclusion in the book. The papers are devoted to all aspects of...

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

This book presents an innovative methodology for the automatic generation of analog integrated circuits (ICs) layout, based on template descriptions and on evolutionary computational techniques. A design automation tool, LAYGEN II was implemented to validate the proposed approach giving special emphasis to reusability of expert design...

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

One of the major challenges for researchers in the field of management science,
information systems, business informatics, and computer science is to develop
methods and tools that help organizations, such as companies or public institutions,
to fulfill their tasks efficiently. However, during the last decade,
...

Forty years have passed since the birth of fuzzy sets, in which time a wealth of theoretical developments, conceptual pursuits, algorithmic...

Learning Classifier Systems (LCS) constitute a fascinating concept at the intersection of machine learning and evolutionary computation. LCS’s genetic search, generally in combination with reinforcement learning techniques, can be applied to both temporal and spatial problem-solving and promotes powerful search in a wide variety of...