# Gauss Therm Algorithm – A Typical Example

Due to the relative newness of Genetic Algorithms (GA) within commercial programming, it is extremely important for any Master’s or PhD student to understand the proper uses of GA in model-control applications. After all, the success of a GA application lies largely on the efficiency of its usage. Without proper usage of GA, a model-control application would be wasted.

Some examples of situations where the GA might be more appropriate than traditional mathematical modeling and control of temperature processes include:

– Hot water heater. GA is generally used as an adjunct to traditional models to achieve the desired results, rather than the other way around. In this case, though, it may be more efficient to use GA for such operations as the initial estimation of potential or actual heat transfer rates through a particular pipe. The model-control application will still be significantly more complex than a traditional model with the same objective function.

– Body temperature. Many geometrical processes lead to heat transfer from one point to another. Since the thermostat is not equipped to process these computations efficiently, a model-control application may be more efficient for such computations. An example of such applications is modeling and controlling the temperature of a particular body part or individual’s body.

– Temperature process. In many cases, a model-control application will only perform mathematical computations in order to come up with a desired outcome for the model itself, rather than performing numerical computations on the physical system.

– Numerical. If the calculations are being performed for modeling and control of temperature process using a GA, it is imperative that there is a numerical method for generating the computations necessary for the model-control application. When dealing with heat, calculations can only be generated using mathematical methods. When dealing with mechanical and thermal effects, numerical methods must be used to generate computations and monitor the results.

– Physical models. While physical models may be more complicated than mathematical models, they are usually more reliable and give a faster response time. This is because mathematical models have an inherent tolerance for error, whereas physical models tend to be more likely to suffer from a failure and be subject to failure. In this case, the simple model-control application can typically be made more complex using a physical model.

– Statistical models. Most real-world applications use statistical methods to achieve predictive, predictive models. It is very difficult to design a mathematical model that can perfectly match the statistical data of real-world applications. In this case, the real-world application must be modeled and controlled using a model-control application that uses a statistical method.

– Thermal. For a model-control application, the initial estimation of temperature and humidity for a particular area is best performed using mathematical algorithms. For some applications, however, it is impractical to perform such computations and so the computation must be performed with a model-control application.

– Geometrical. Sometimes, the problems are much more complicated than the problems that arise with geometric computations. In this case, the need for a Gaussian process is often necessary.

– Hot water heater. Although it may be easier to use a model-control application for hot water heater, there are always some special cases where it may be more efficient to use a Gaussian process. In such cases, it is generally wiser to use a regular model instead of a Gaussian process, so that the problem is done in the right manner.

The hot water heater is one of the examples of situations where GA can be of benefit. Unlike the geometrical problems in the heating process, which do not require specialized methods to make them feasible, the heat transfer from a hot water heater has a set of intrinsic properties. The system is already in a certain state; it needs to be broken down into its constituent parts before you can actually solve the problem.