The preliminary experiments seem to confirm the advantage of using the adaptive mutations. By incorporating search operators themselves into the evolutionary search, more information about the fitness landscape obtained during previous search can be kept and used to bias subsequent search towards more promising regions. A related issue to adaptive search operators is how to characterize a fitness landscape so that some of the characteristics can be measured and used by adaptive operators in order to search efficiently. This is still an open issue although there has been a lot of research efforts devoted to it. Every search algorithm, except for uniformly random search, introduces some kind of bias into its search.
Different algorithms have different biases.
Evolutionary algorithms with different operators also have different biases. There have been some discussions on the biases introduced by various crossover operators in the genetic algorithm literature. Since we still don't know how to characterize a fitness landscape so that some biases can be used to search efficiently, it is very difficult to determine which search algorithm should be applied to a problem because it has appropriate biases. The scarceness of a priori knowledge about a problem makes the design of a good search algorithm even more difficult. In addition to the study of adaptive search operators where biases can be changed automatically to achieve higher search efficiency, another way to reduce the possible detrimental effects of biases introduced by an algorithm is to use a hybrid algorithm which includes different biases introduced by several different algorithms in the hope that the best bias would always be used in search.