Novel evolutionary algorithms for supervised classification problems: an experimental study

Evolutionary Algorithms (EAs) are populationbased, stochastic search algorithms that mimic natural evolution. Over the years, EAs have been successfully applied to many classification problems. In this paper, we present three novel evolutionary approaches and analyze their performances for synthesizing classifiers with EAs in supervised data mining scenarios. The first approach is based on encoding rule sets with bit string genomes, while the second one utilizes Genetic Programming (GP) to create decision trees with arbitrary expressions attached to the nodes. The novelty of these two approaches lies in the use of solutions on the Pareto front as an ensemble. The third approach, EDDIE-101, is also based on GP but uses a new, advanced fitness measure and some novel genetic operators. We compare these approaches to a number of well-known data mining methods, including C4.5 and Random-Forest, and show that the performances of our evolved classifiers can be very competitive as far as the solution quality is concerned. In addition, the proposed approaches work well across a wide range of configurations, and EDDIE-101 particularly has been highly efficient. To further evaluate the flexibility of EDDIE-101 across different problem domains, we also test it on some real financial datasets for finding investment opportunities and compare the results with those obtained using other classifiers. Numerical experiments confirm that EDDIE- 101 can be successfully extended to financial forecasting