Real-world problems often contain nonlinearities, relationships, and uncertainties that are too complex to be modeled analytically. In these scenarios, simulation-based optimization is a powerful tool to determine optimal system parameters. Evolutionary Algorithms (EAs) are robust and powerful techniques for optimization of complex systems that perfectly fit into this concept. Since evolutionary algorithms require a large number of time expensive evaluations of candidate solutions, the whole process of optimization can take huge CPU time. In this chapter, .NET platform for distributed evaluation using WCF (Windows Communication Foundation) Web services is presented in order to reduce computational time. This concept provides parallelization of evolutionary algorithms independently of geographic location and platform where evaluation is performed. Hydroinformatics is a typical representative of fields where complex systems with many uncertainties are studied. Application of the developed platform in hydroinformatics is also presented in this chapter.
Distributed evolutionary algorithms with hierarchical evaluation
