# Neuroevolution for Parameter Adaptation in Differential Evolution
## 4 Discussion  
The results of the computational experiments presented in the previous section demonstrate the possibility of applying a neuroevolutionary approach to the problem of the automatic design of parameter adaptation techniques. The results also show that there is a certain overfitting to the problems which are used for training. Thus, to receive more generalized solutions capable of controlling the parameters of DE efficiently, more training cases should be considered. Moreover, these training scenarios should be diverse, i.e., represent different cases which may occur during the search by DE. In addition, the dependence on the available computational resource should be analyzed: In the performed experiments, the computational resource during the whole training process was fixed, and the testing was performed with the same resource.  
Figure 4: Process of parameter adaptation with solution from run 49, CEC 2021 test functions, \(10D\), biased, rotated and shifted functions.  
The approach used in this study follows the ideas of automatic heuristic generation presented in several studies, such as [64; 65; 66; 67]. These hyper-heuristics are aimed at designing solutions for complex problems with heuristic approaches, and one of our previous studies has shown that it is possible to use genetic programming for the problem of designing parameter adaptation techniques [22]. The hyper-heuristics are important for several reasons: They not only create a solution to the problem (which could be in the form of an algorithm, graph or neural network), which could be directly used, but also discover new, non-trivial ways of solving problems. This is possible due to the fact that the algorithms are random and are not biased by the human perception of the problem. As an example, to the best of our knowledge, there are no efficient parameter adaptation techniques for DE, which would use success ratio values directly, and here, it appeared to be informative for generating \(F\) and \(Cr\). Moreover, such values as the ratio of an index of an individual in the population sorted by fitness is an example of an input value, which is quite difficult to apply for parameter adaptation, simply because its influence is unclear. However, the NEAT approach, for example, HA-NEAT-DBM used in this study, is able to make use of these values and apply them to the samples \(F\) and \(Cr\). Of course, this does not mean that these values are reliable information sources; however, the analysis of solutions may help researchers gain insights into how these values could be of use and what the internal mechanisms could be blocking the algorithm from achieving better search results.  
In this manner, applying hyper-heuristic approaches to parameter adaptation in DE could be considered as a knowledge extraction procedure, or even 'algorithm mining' (an analogy for data mining). An important part of this is that the computational experiments are performed without a high-level theory, which would describe the possible outcomes and predict the results. Here, the experiment has 'a life of its own', where massive computation and application of evolutionary principles lead to automatic knowledge extraction. Such studies are known to follow the concept of new experimentalism [68], which is a part of any experimental research in the area of evolutionary computation.  
Taking into account all of the above, the application of hyper-heuristics to parameter adaptation is a research direction where new and important results could be found for the whole evolutionary algorithms area, and the particular learning algorithm used is not so important: should it be genetic programming, neuroevolution, fuzzy logic system or something else, while it allows the direct analysis of solutions, it can be considered as a knowledge extraction method.