Abstract
Evolutionary Algorithms (EAs) have been applied to many optimisation problems, among which those with high order are difficult for EAs. The higher the order, the steeper the curve around the optimum is, therefore the more difficult it is. This paper introduces Transfer Learning (TL) aided EAs to conquer the high-order problems more efficiently and effectively by optimum transfer from the low-order problem (as source domain) to high-order problem (as the target domain). The experiments validated this method by comparison of the average number of the convergence generation and an impressive feature was observed: this method is robust against the difficulties of the problems. This method is not only significant for high-order problems, but also useful for other difficult problems by borrowing optimum from other feature-similar easy problems.
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