Abstract
The design of models efficiently predicting the performance of a particular genetic algorithm on a given fitness landscape is a very important issue of practical interest. Virtual Genetic Algorithms (VGAs) constitute a statistical approach aimed at this objective. This work describes different improvements to the standard VGA model. These improvements are based on the use of a more representativ e dataset for the statistical analysis, the partitioning of this dataset into separate prediction models, and the utilization of a more sophisticated statistical model to grasp the distribution of fitnesses. The empirical evaluation of this enhanced model shows a more accurate fitness prediction. Furthermore, fast qualitative assessment of parameter changes is shown to be possible.
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Nogueras, R., Cotta, C. (2001). Using Statistical Techniques to Predict GA Performance. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_85
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DOI: https://doi.org/10.1007/3-540-45720-8_85
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