Evolutionary Computation for Big Data Mining

Evolutionary computation (EC) has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. The EC techniques are a subset of artificial intelligence, but they are slightly different from the classical methods in the sense that the intelligence of EC comes from biological systems or nature in general. The efficiency of EC is due to their significant ability to imitate the best features of nature which have evolved by natural selection over millions of years. The main theme of this presentation is about EC techniques and their application to real-world problems. On this basis, the presentation is divided into two separate sections including (big) data mining, and global optimization. First, applied evolutionary computing in data mining field will be presented, and then their new advances will be mentioned such as big data mining. Here, some of my studies on big data mining and modeling using EC and genetic programming, in particular, will be presented. As case studies, EC application in some real-world problems will be introduced. And then, application of EC for response modeling of a complex engineering system under seismic loads will be explained in detail to demonstrate the applicability of these algorithms on a complex real-world problem. In the second section, the evolutionary optimization algorithms and their key applications in the optimization of complex and nonlinear systems will be discussed. It will also be explained how such algorithms have been adopted to the real-world problems and how their advantages over the classical optimization problems are used in action. Optimization results of large-scale systems using EC will be presented which show the applicability of EC. Some heuristics will be explained which are adaptable with EC and they can significantly improve the optimization results.