What is a genetic algorithm?
The genetic algorithm was made socially acceptable by John H. Holland. Problem indications in the binary domain form the basis here, which is why genotype-phenotype mapping is used. This means nothing other than that binary candidates in the solution domain must first of all be transformed and changed.
The aim is to ensure evaluation with the existing fitness function. Due to this peculiarity, they form a parallel to the biological counterpart. Our genetic material resembles quasi binary numbers due to 4 coded existing nucleic acids. Recombination and mutations are thus easily possible.
The phenotype (appearance) is created through processes and procedures that consist of many steps. Genotype-phenotype mapping is similar. Binary representations are clearly suitable for rapid processing in complex computer systems. Roughly summarised, one can say that genetic algorithms want to copy and imitate the basic principle of biological evolution in some form.
Application of the genetic algorithm
When it comes to solving problems, a genetic algorithm can be useful. When building bridges, a genetic algorithm can optimise the shape, the weight of components or even their position. At neural networks these also find an optimal application, for example when adjusting the existing clamp potential vector.
Things to know
In various initial situations, the aim is always to achieve a desired result at some point with the help of genetic algorithms, which is realised by constantly sorting out and selecting as well as changing information. But the results that are achieved in this way are not always optimal and tailored to the exact problem; there are sometimes deviations.
The reason for this is that the respective changes and selections may not have been pronounced enough; the operations, which are genetic, are to blame. Instead of presenting optimal solutions, suboptimal results are thus produced. There is also another disadvantage. A genetic algorithm always generates a large computational effort, thus also a longer runtime. However, this can be reduced and minimised through optimisation.
Economy and everyday life
When it comes to evolutionary, effective mechanisms, the term genetic algorithms comes up again and again. These have been gaining in importance for decades, as they find a useful application in the banking sector or stock market trading, for example.
Especially when it comes to hedging decisions or valuations of portfolios of different types, genetic algorithms are in demand like never before. They are also used all the time in large areas such as medicine, digital healthcare and research.
What does the future hold?
Due to increasingly complex processing procedures within IT systems, genetic algorithms are also becoming more and more extensive. It can therefore happen that faulty mutations creep in and unusable populations are created.
Thus, it is important that the processes of this kind of evolution continue to be optimised so that, for example, commercial enterprises or the research sector that use algorithms in this way in everyday life do not suffer any disadvantages. The bottom line is that genetic algorithms are a good thing. They make processes efficient, faster and ensure that operations run as smoothly as possible. Complex calculations like this could never be done manually by humans, but genetic algorithms make them possible.
