An application of a GA with Markov network surrogate to feature selection
Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by evolutionary algorithms. This is of particular interest with expensive fitness functions where the time taken for building the model is outweighed by the savings of using fewer function evaluations. In this article, we show how a Markov network model can be used as a surrogate fitness function for a genetic algorithm in a new algorithm called Markov Fitness Model Genetic Algorithm (MFM-GA). We thoroughly investigate its application to a fitness function for feature selection in Case-Based Reasoning (CBR), using a range of standard benchmarks from the CBR community. This fitness function requires considerable computation time to evaluate and we show that using the surrogate offers a significant decrease in total run-time compared to a GA using the true fitness function. This comes at the cost of a reduction in the global best fitness found. We demonstrate that the quality of the solutions obtained by MFM-GA improves significantly with model rebuilding. Comparisons with a classic GA, a GA using fitness inheritance and a selection of filter selection methods for CBR shows that MFM-GA provides a good trade-off between fitness quality and run-time.
markov networks surrogate models genetic algorithms fitness approximation case base reasoning