Examples of these challenges, including limited computing resources to run a simulation, complex surface response of the simulation input-output, etc. In this difficulty, classical optimization and analysis methods to perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in these settings. reduction in expensive evaluation (modeling, variable-fidelity, fitness inheritance), a framework for optimization (model management, control complexity, model selection), parallelization algorithm (the implementation issue clusters, grids, parallel machines), systems integration experts and human-system interfaces, single and multiple objective algorithms, data mining and statistical analysis, analysis of real-world cases (such as multidisciplinary design optimization). Edited book gives a good treatment of the theoretical and real world insight gained through experience, all donated by leading researchers in their respective fields.