CITIS 2012 - Workshop on Computational Intelligence Techniques in Industrial Systems

Computational intelligence research extends over a wide area in almost all the engineering applications providing a complementary view of implementation of these heuristic methods. Computational intelligence (CI) is closely related with soft computing a combination of artificial neural networks, fuzzy logic and genetic algorithms, connectionist systems such as artificial intelligence and cybernetics.
Computational Intelligence is a collection of heuristic algorithms such as evolutionary algorithms, fuzzy systems, and neural networks. In addition, computational intelligence also embraces techniques that use Swarm intelligence, Fractals and Chaos Theory, Artificial Intelligence and artificial immune systems. Several applications applying computational intelligence paradigms are still in development. Although computational intelligence and its various techniques are reputable fields, the new applications of using computational intelligence can be regarded as an emerging field.
This workshop presents Computational Intelligence paradigms in Industrial application areas to show the various innovations, which bridges the theoretical and practical gap between the attempts to learn how to apply Computational Intelligence practically.
The workshop welcomes original submissions on all aspects of Computational Intelligence Techniques, which include (but are not limited to) the following topics and themes:
Machine Learning Techniques
Genetic Algorithms
Image Processing
Soft computing applications
Reconfigurable computing
Optimization Techniques and Probabilistic Models
Chaos Computing
Expert system applications
Medical Industrial applications
Applications of Genetic Evolutionary computation
Evolutionary algorithms
Modelling and simulation of Industrial processes
Evolutionary algorithms for designing generic machine
Evolutionary algorithms for designing generic function optimization
(Meta-level) evolutionary algorithms for designing other (base-level) evolutionary algorithms
Empirical comparison of different hyper-heuristics