Artificial Intelligence has been widely used during the last two decades and has remained a highly-researched topic, especially for complex real-world problems. Evolutionary Computation (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 central theme of this presentation is about EC techniques and their application to complex real-world problems. On this basis, first I will talk about an automated learning approach called genetic programming. Applied evolutionary learning will be presented, and then their new advances will be mentioned. Here, some of my studies on big data analytics and modelling using EC and genetic programming, in particular, will be presented. Second, EC will be presented including key applications in the optimization of complex and nonlinear systems. It will also be explained how such algorithms have been adopted to engineering problems and how their advantages over the classical optimization problems are used in action. Optimization results of large-scale towers and many-objective problems will be presented which show the applicability of EC. Finally, heuristics will be explained which are adaptable with EC and they can significantly improve the optimization results.
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