The First IEEE Symposium on Foundations of Computational Intelligence (FOCI'07)
1-5 April 2007, Honolulu, Hawaii, USA
Sponsored by IEEE Computational Intelligence Society
SCOPES and TOPICS
The First IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI'07) will be held as part of the First IEEE Symposium Series on Computational Intelligence (IEEE-SSCI'2007) to be held at the Hilton Hawaiian Village Beach Resort & Spa in famous Waikiki, Honolulu, Hawaii, USA, on 1-5 April 2007. IEEE FOCI 2007 will focus on theoretical and practical foundations of computational intelligence, including but not limited to neural networks, fuzzy logic, evolutionary computation, swarm intelligence and other machine learning methods.
In spite of numerous successful applications of computational intelligence techniques in business and industry, such successes (or the occasional lack thereof) are not always fully understood. There is an urgent need in more rigorous studies of the foundations of computational intelligence, so that new breakthroughs can be fostered. This symposium, IEEE FOCI'07, provides an ideal forum for those who are interested in the fundamental issues of computational intelligence to exchange their ideas and present their latest findings. The symposium will put equal emphasis on theoretical and practical work as long as it addresses the foundations of computational intelligence. The topics coverd by FOCI'07 include, but are not limited to:
- Non-standard fuzzy sets (e.g., type-2, interval-valued, random-fuzzy, fuzzy-random, etc.)
- Granular computing
- Computing with words
- Aggregation/fusion
- Fuzzistics (fuzzy sets +statistics)
- Uncertainty
- Decision-making
- General theoretical issues
- Applications that use new FL theory
- Computational time complexity of evolutionary algorithms (EAs)
- Characterisation of EA-hard and EA-easy problems
- Novel measures and techniques for characterising EA-hardness
- Interactions between search operators and representation
- Fitness landscape analysis
- Convergence and convergence time of multi-objective evolutionary algorithms
- Evolutionary game theory
- Interactions Between Learning and Evolution
- Generalisation in neural, fuzzy and evolutionary learning
- Complexity in Adaptive Systems
- Self-adaptation
- Computation for cognitive and brain function
- General theoretical issues
- Autonomous mental development
- Biologically inspired computing system
- Cognitive science for information engineering
- Cognitive system modeling
- Computational modeling of large scale brain process
- Computational neuroscience
- Data analysis and pattern recognition
- Information geometry
- Knowledge discovery
- Lifelong learning
- Modeling of human thinking/decision making
- Social intelligence
- Statistical learning theory
- Statistical physics of information theory
- Supervised, unsupervised and reinforcement learning
