Track: Applications of Evolutionary Computation

 

This track is part of C³S²E'2013.

 

Evolutionary Computation (EC) includes a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are considered global optimization methods as they can effectively explore very large search spaces without becoming caught by local optima. Different main lines of research of EC have evolved during the last 40 years: genetic algorithms, evolutionary strategies, and evolutionary programming. These techniques are being increasingly widely applied to a variety of problems, ranging from theoretical studies to practical applications. In fact, in the last years the uses of EC in real word applications increasingly grow and it’s currently an important area of research.

 

The objective of this track is to encourage research in the use of Evolutionary Computation techniques on different types of applications. This track welcomes original work describing advances in applications of evolutionary computation and related techniques, including genetic algorithms, evolution strategies, evolutionary programming, memetic algorithms, genetic programming, ant colony optimization, co-evolutionary algorithms, artificial immune systems, particle swarm optimization, multi-objective evolutionary algorithms and classifier systems in different areas of application. Areas of interest include but are not limited to:

 

Arts and music, Automation and control, Bioinformatics, Communication networks, Energy, Finance and economics, Industry and commerce, Medical and Health, Risk management and defense, Robotics, Security, reliability, and fault-tolerance, Video games.