This work introduces the Unscented Genetic Algorithm (U-GA), which combines ideas from evolutionary computation and Kalman filters to devise a novel approach to solve GA-hard problems. The approach is justified based on how other Bayesian methods make strong assumptions on data, which could limit their performance in the long run. U-GA applies theory from unscented Kalman filters to relax these assumptions via Monte-Carlo simulation. The algorithm is explained in detail, showing how unscented Kalman filters equations could be adapted for the evolutionary computation framework. In the experiments, the proposed approach is compared to Bayesian optimization algorithm (BOA) and genetic algorithms (GAs) to investigate the strengths and limitations of U-GA. The results show how U-GA attains better performance than the benchmarks, even when the problem size is increased. Also, U-GA attained a considerable speed-up (around 400%) when compared with similar methods.

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