Any topic related to Computational Intelligence, mainly but not limited to the following:

Problems

Single List for all tracks:

– Foundations of Computational Intelligence;
– Analysis & Identification;
– Modeling & Design;
– Representation & Interfacing;
– Methods & techniques of CI Hybridization;
– Simulation & Estimation;
– Signal & Sensor Processing;
– Machine learning and pattern recognition;
– Time series forecasting;
– Classification & Clustering;
– Search & Optimization (combinatorial, stochastic, dynamic, multimodal, multi-objective, etc);
– Constraint handling (Routing, Scheduling, Timetabling, allocation Neighboring, Placement, etc);
– Information retrieval & Ambient intelligence
– Decision Problems;
– Cognitive Robotics;
– Image Processing & Computer Vision;
– Bio & Medical Informatics – Computational biology;
– Information & Network security;
– Data & Web Mining;
– E-commerce, E-Procurement & E-Government;
– Telecommunications and Networking;
– Energy generation and energy dispatch;
– Parallelization & Hardware Implementations.

Approaches

(1) Evolutionary & Swarm Computation:
– Evolutionary computation;
– Swarm intelligence;
– Artificial immune systems;
– Novel metaheuristics and hyperheuristics;
– Memetic and Collective intelligence;
– Nature and Bio-inspired methods;
– Artificial life.

(2) Neural & Learning Systems:
– Machine learning;
– Neural Computation (+Weightless Systems);
– Complex systems;
– Wavelets;
– Molecular and quantum computing;
– Brain-machine interfaces;
– Network Sciences;
– Local search methods.

(3) Fuzzy & Stochastic Modeling:
– Fuzzy logic;
– Fuzzy optimization and design;
– Fuzzy pattern recognition;
– Fuzzy control & decision making/support;
– Rough sets;
– Uncertainty analysis;
– Fractals;
– Game theory;
– Social Simulation;
– Multi-agent systems;
– Symbolic Systems;
– Grey systems.

Technologies

(1) Evolutionary & Swarm Computation:
– Ant colony optimization;
– Particle swarm optimization;
– Fish School Search;
– Bee colony optimization;
– Genetic programming and Genetic algorithms;
– Cultural Algorithms and Co-Evolution;
– Evolutionary Strategy and Differential Evolution;
– Evolutionary design & scheduling;
– Danger theory and network immune systems;
– Firefly optimization;
– Glowsworm Swarm Optimization;
– Cuckoo search;
– Herd optimization; etc.

(2) Neural & Learning Systems:
– Cognitive systems and applications;
– Computer vision;
– Hardware Implementations;
– Web intelligence;
– Natural language processing & Speech understanding;
– Supervised and Unsupervised Learning;
– Semi-supervised and Weakly Supervised Learning;
– Support vector machines;
– Local Search Methods (Tabu search, iterated search, etc.)
– Reinforcement Learning
– Neuroscience and biologically inspired control;
– Distributed intelligent systems; etc.

(3) Fuzzy & Stochastic Modeling:
– Fuzzy sets & Type-2 fuzzy logic;
– Approximate reasoning;
– Rough sets & data analysis;
– Case-Based Reasoning;
– Expert systems;
– Knowledge engineering.
– Adaptive Dynamic Programing & Control;
– Convergence and performance analysis;
– Bayesian methods;
– Monte-Carlo methods and variations;
– Markov decision processes; and,
– Other Statistical learning.