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Today numerous variants of heuristic optimization algorithms are used to solve different kinds of optimization problems. This huge variety makes it very difficult to reuse already implemented algorithms or problems. In this paper the authors describe a generic, extensible, and paradigm-independent optimization environment that strongly abstracts the process of heuristic optimization. By providing a well organized and strictly separated class structure and by introducing a generic operator concept for the interaction between algorithms and problems, HeuristicLab makes it possible to reuse an algorithm implementation for the attacking of lots of different kinds of problems and vice versa. Consequently HeuristicLab is very well suited for rapid prototyping of new algorithms and is also useful for educational support due to its state-of-the-art user interface, its self-explanatory API and the use of modern programming concepts. [ABSTRACT FROM AUTHOR]

Selection of an optimal subset of classifiers in designing classifier ensembles is an important problem. The search algorithms used for this purpose maximize an objective function which may be the combined training accuracy or diversity of the selected classifiers. Taking into account the fact that there is no benefit in using multiple copies of the same classifier, it is generally argued that the classifiers should be diverse and several measures of diversity are proposed for this purpose. In this paper, the relative strengths of combined training accuracy and diversity based approaches are investigated for the plurality voting based combination rule. Moreover, we propose a diversity measure where the difference in classification behavior exploited by the plurality voting combination rule is taken into account. [ABSTRACT FROM AUTHOR]

The paper proposes an implementation of the population learning algorithm designed to solve instances of the quadratic assignment problem. A short overview of the population- learning algorithm and a more detailed presentation of the proposed implementation is followed by the results of computational experiments carried. Particular attention is given to investigating performance characteristics and convergence of the PLA. Experiments have focused on identification of the probability distribution of solution time to a sub-optimal target value. [ABSTRACT FROM AUTHOR]