FOCI 2016 - IEEE Symposium on Foundations of Computational Intelligence(IEEE FOCI'16)

The 2016 IEEE Symposium on Foundations of Computational Intelligence (FOCI 2015) will take place as part of the IEEE Symposium Series on Computational Intelligence (SSCI 2016).
Computational intelligence techniques are widely used to tackle real-world problems due to their numerous successful applications. However, the reasons behind these successes are often not well understood. A solid theoretical foundation of computational intelligence techniques explains the reasons behind the success of these methods. Furthermore, theoretical analyses lead to the understanding of which problems are solved efficiently by a given technique and which are not. Amongst the benefits to practitioners a solid theoretical understanding (a) provides guidance on the choice of the best technique for the problem at hand, (b) helps to identify optimal parameter settings and ultimately (c) aids the design of more effective techniques.
IEEE FOCI'16 will focus on fundamental theoretical foundations of (but not limited to) the three main branches of computational intelligence, Neural Networks and other machine learning methods, Fuzzy Logic and Evolutionary Computation. Although the symposium's main interest is in theoretical foundations, computational studies of a foundational nature are also welcome.
As in the previous SSCI editions, accepted papers will be included in the Conference Proceedings Citation Index.
IEEE FOCI'16, provides an ideal forum for those who are interested in the foundational issues of computational intelligence to exchange their ideas and present their latest findings. Participants of FOCI'16 will also benefit from the interaction at one location with the participants of the several other symposia running concurrently at IEEE SSCI 2016, each highlighting various aspects of computational intelligence. As a whole, this international event will attract top researchers, practitioners, and students from around the world to discuss the latest advances in the field of computational intelligence.
NON EXCLUSIVE LIST OF TOPICS
Fuzzy Logic
Non-standard fuzzy sets
Granular computing
Computing with words
Aggregation/fusion
Fuzzy sets and statistics
Uncertainty
Decision-making
General theoretical issues
Generalisation in neural, fuzzy and evolutionary learning
Fuzzy logic and fuzzy set theory
Lattice theory and multi-valued logic
Approximate reasoning
Type-2 fuzzy logic
Rough sets and random sets
Fuzzy mathematics
Fuzzy measure and integral
Possibility theory and imprecise probability
Neural Networks and other machine learning techniques
Neural computation
Self-organizing maps
Recurrent networks
Multilayer perceptrons
Recursive deterministic perceptrons
Evolutionary neural networks
Neural networks for pattern recognition
Neural netwoks for prediction and optimization
Neural networks for principal component analysis
General regression neural networks
Neural networks as/and fuzzy systems
Radial basis functions
Learning theory
Reinforcement learning
Generalization in neural networks
Evolutionary Computation
Theoretical foundations of bio-inspired heuristics
Exact and approximation runtime analysis
Fixed budget computations
Black box complexity
Self-adaptation
Population dynamics
Fitness landscape and problem difficulty analysis
No Free Lunch Theorems
Statistical approaches for understanding the behaviour of bio-inspired heuristics
Computational studies of a foundational nature
All bio-inspired search heuristics will be considered for all problem domains including
Combinatorial and continuous optimization
Single-objective and multi-objective optimization
Constraint handling
Dynamic and stochastic optimization
Co-evolution and evolutionary learning
Programme Chairs
Manuel Ojeda-Aciego
Universidad de Málaga, Spain. Email:aciego-AT-ctima.uma.us
Pietro S. Oliveto
The University of Sheffield, UK. Email:p.oliveto-AT-sheffield.ac.uk
Leonardo Franco
Universidad de Málaga, Spain. Email:lfranco-AT-lcc.uma.es