## 3 Implementation of the OAC-DES  
Many of the problems with which people are confronted today are dynamic in nature. This means that environmental conditions are not static, but change over time. In EAs, these changes often affect the real-time fitness function, whereby the optimal solution at one time is non-optimal at another and vice versa. Algorithms for solving dynamic problems must therefore [3]:  
* be aware of the changing environment,
* respond to environmental changes efficiently.  
Usually, the first condition can be met by using additional memory, where a redundant individual (also called a sentinel) is used in the population. The change in the fitness landscape is detected due to the change of the fitness function value. Indeed, the fitness value is changed after the environment has been changed. The prerequisite for the second condition is sufficient population diversity that allows the already converged population at the most recent optimum to search for the new optimum in a changed fitness landscape.  
Classical EAs suffer from a lack of reactivity due to maintaining the population of the solution. Although this population allows EAs to find a near-optimum solution in many cases, the time needed for finding it is unsuitable for systems in real situations, in which the so-called feedback control systems must respond to a sequence of cause-and-effect relationships among the system variables. The theory of feedback control systems [33] asserts that for mechanical time constants of non-linear robotic mechanisms, a suitable sampling time should be between \(T_{sv}\in[1,5]\) ms. This means that when the EAs are applied in the feedback control system, small populations with rapid responses should be achieved to limit the time complexity. Consequently, the most suitable EAs for this purpose are most certainly Evolution Strategies (ES) [34] that are, in their simplest configuration (1+1)-ES, capable of maintaining only one population member, and are therefore not too time complex. Additionally, ES also support self-adaptation, which is indispensible in achieving the fast response time of this feedback system.  
Unfortunately, the (1+1)-ES cannot be run autonomously on the computer system, because of fitness function evaluation. This evaluation actually demands an immediate response from the feedback control system, where a behavior of the generated solution needs to be estimated. This means that the wrong solutions generated by (1+1)-DES could cause faulty operation of the control system, resulting in damage. At the same time, however, changing the test values of the real control system online is time consuming and therefore inappropriate for real-time processing. Therefore, in our study the evaluation is not performed on the real system directly, but is predicted using ANN-simulation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristics** & **CPU** & **CLA** \\ \hline Processing speed & 400 MIPS & 400 MIPS \\ Clock Frequency & 200 MHz & 200 MHz \\ Flash size & 1024 kB & 512 kB \\ RAM size & 164 kB & 132 kB \\ No. of ADC ch. & up to 24 ch. & up to 24 ch. \\ Programming language & C, ASM & ASM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common characteristics of TMS320F28377S.  
However, reliable prediction by an ANN-simulator is possible only when it is conducted on the regularly learned ANN. Consequently, ANN-learning must be launched before ANN-simulation in each sampling time. The OAC-DES consists of three algorithms:  
* ANN-learning,
* (1+1)-DES,
* ANN-simulation.  
In the remainder of this paper, these OAC-DES algorithms are presented in detail.