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In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.

Differentiation Evolution (DE) is an EvolutionaryAlgorithm (EA) for solving function optimization problems. In order to renew the population in EA, there are two generation models. The first one is "discrete generation model", and the second one is "continuous generation model". Conventional DEs have been based on the discrete generation model in which the current generation's population is replaced by the next generation's population at a time. In this paper, a novel DE based on the continuous generation model is described. Because a newborn excellent individual is added to an only population and can be used immediately to generate offspring in the continuous generation model, it can be expected that the novel DE converges faster than the conventional ones. Furthermore, by employing the continuous generation model, it becomes easy to introduce various survival selection methods into DE. Therefore, three survival selection methods are contrived for the novel DE based on the continuous generation model. Finally, the effects of the generation model, the survival selection method, the reproduction selection method, the population size and their interactions on the performance of DE are evaluated statistically by using the analysis of variance (ANOVA).