Implementation of Evolution Strategies for Classifier Model Optimization

Mahmud Dwi Sulistiyo, Rita Rismala


Classification becomes one of the classic problems that are often encountered in the field of artificial intelligence and data mining. The problem in classification is how to build a classifier model through training or learning process. Process in building the classifier model can be seen as an optimization problem. Therefore, optimization algorithms can be used as an alternative way to generate the classifier models. In this study, the process of learning is done by utilizing one of Evolutionary Algorithms (EAs), namely Evolution Strategies (ES). Observation and analysis conducted on several parameters that influence the ES, as well as how far the general classifier model used in this study solve the problem. The experiments and analyze results show that ES is pretty good in optimizing the linear classification model used. For Fisher’s Iris dataset, as the easiest to be classified, the test accuracy is best achieved by 94.4%; KK Selection dataset is 84%; and for SMK Major Election datasets which is the hardest to be classified reach only 49.2%.

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