What are Genetic Algorithms?

In operations research and computer science, a genetic algorithm is usually a powerful but simple form of mathematical meta theory that refers to the smaller class of overall evolutionary algorithms. The term “algorithm” comes from the Greek word “aktos,” which means “doing.” In computer sciences, an algorithm is defined as a procedure or set of procedures. Algorithms are used in all forms of scientific research and industry, including genetics and evolution. For instance, the DNA analysis results of a study can be considered an algorithm, as is the method of gene mapping. The application of algorithms to specific biological situations is called particularization.

What are Genetic Algorithms

The first part of a genetic algorithm is the basis for implementing a programming language such as C++. This programming language allows programmers to describe biological processes and their effect on genetic programs. The genes can be coded in different forms, such as amino acids or nucleic acids and can be expressed from a protein through various cells within the body. An expression can occur in all the cells or some of them, depending on the type of gene being expressed. The expression usually involves transcription.

The second step in genetic algorithms is an expression, where the genetic programs expressed by transcription become translated into actual proteins. Some genetic programs control the development of other genetic programs. Programs expressed by transcription, therefore, control coding relevant to ancestry. They also control the activity of genes and thus regulate biological processes. Gene mapping is a topic in its own right, but we will only consider examples of genetic programs expressed by transcription for our purposes.

A genetic algorithm addresses some broad questions about biological systems. It asks how coding can be structured and its effect on the function and organization of DNA. It asks what functional genes are coded for and how many total copies of each locus exist. It asks what relationship exists between unrelated loci that differ on the coding strand and whether rearrangements of these unrelated loci can cause an overall imbalance in the desired trait. Finally, it asks how the genetic code influences disease processes.

These are broad questions, and the answers provided by genetic algorithms are not detailed and precise. However, even a basic understanding of genetic codes and their relationship to genetic algorithms pose many interesting and difficult questions. In my opinion, the most interesting and difficult questions related to the nature of evolution, which to some extent is still the subject of ongoing research. Additionally, some of the more basic questions address population genetics and gene pool maintenance.

Most genetic algorithms translate a genetic code into a numerical value representing a characteristic of that code. For example, we all know that the human genome contains information on over fifty thousand genes. Some of these genes have regulatory domains that are active in humans, while others are non-functional. This latter class represents the remainder of the genetic codes. Thus, the geneticists seek to find, map, and identify these non-functional domains and try to unravel the nature of protein-based diseases.

Interestingly the study of genetic algorithms also contributes something very important to medical science. It shows that although we may never understand every single gene’s function, it is nevertheless possible to discover the overall effect of the genetic code on the whole body and the outcome of a disease. Thus the knowledge of the entire coding system leads to advancements in genetic counselling and perhaps even in developing drugs for human disorders. This is because the code influences and controls the way that various components of the body function together. And this means that understanding the function of the coding system allows one to realize how it influences and controls specific components, thus allowing the physician to treat each component separately.

Today we are discovering new applications of genetic algorithms. And this pace shows no sign of slowing down. Rather it is increasing as our knowledge of the genetic code grows. It will continue to play a critical role in the future of medicine.

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