ECADA 2013 - 3rd Workshop on Evolutionary Computation for the Automated Design of Algorithms

Although most evolutionary computation techniques are designed to generate specific solutions to a given instance of a problem, some of these techniques can be explored to solve more generic problems. The main objective of this workshop is to discuss evolutionary computation methods for generating generic algorithms and/or heuristics. These methods have the advantage of producing solutions that are applicable to any instance of a problem domain, instead of a solution specifically produced for a single instance of the problem. The areas of application of these methods may include, for instance, data mining, machine learning, optimization, bioinformatics, image processing, economics, etc.
The workshop welcomes original submissions on all aspects of Evolutionary Computation for Designing Generic Algorithms, which include (but are not limited to) the following topics and themes:
Evolutionary algorithms for designing generic combinatorial optimization algorithms or heuristics
Evolutionary algorithms for designing generic machine learning algorithms or heuristics
Evolutionary algorithms for designing generic function optimization
Evolutionary algorithms for designing generic algorithms or heuristics for bioinformatics
(Meta-level) evolutionary algorithms for designing other (base-level) evolutionary algorithms
Empirical comparison of different hyper-heuristics
Theoretical analyses of hyper-heuristics
Automatic selection of algorithms' building blocks as a preprocessing step for the use of hyper-heuristics
Analysis of the trade-off between generality and effectiveness of different heuristics algorithms or heuristics produced by hyper-heuristics
Real-world applications of hyper-heuristics