Evolutionary algorithms (EAs) are adaptive methods for finding problem solutions (models, knowledge) based on principles of biological evolution by natural selection, genetic variation, and 'survival of the fittest' (see Figure 13 and Evolutionary Algorithms). Holland provided the theoretical framework for the development of genetic and evolutionary algorithms that are being widely used for pattern recognition, forecasting, knowledge discovery, optimum control, and parallel processing. Useful guides for history, current developments, and applications of genetic and evolutionary algorithms are provided by Goldberg, Mitchell, and Back et al.
Successful implementations of EA as tools for solving complex economic and engineering problems have stimulated their application to solving ecological problems, which exhibit highest complexity. They allow to induce predictive models from ecological data sets similar to supervised ANN but rather than lacking an explicit model representation as typical for ANNs, EAs are
Randomly create population of solutions ¡¡pi
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