Genetic Algorithms are based on the biological evolution of Darwinian Theory. In the biological context, all living organisms have cells and cell consists of chromosomes. Chromosomes have DNA material which carries genetic traits such as the color of eyes, hair color etc. Genes from parents during reproduction, are combined to make a new genetic combination. The offspring carries traits from both the parent. During a process called mutation, some traits fail to copy from one of the parents and the other set of the gene is used basically giving birth to a new set of DNA, new species. The individual which has better traits of survival is best explained as Darwin’s theory (Survival of the fittest) also called as Natural Selection. Now the following article will throw a light on Genetic Algorithm in computer coding which has brought a revolutionary change.
What is Genetic Algorithm?
Genetic Algorithms (GA) is a search-based optimization technique based on Darwin theory of Natural Selection. It is widely used to find the optimal or nearly optimal solutions to a tedious problem. It is, for example, to find the solutions through permutation and combination.
Why Genetic Algorithm?
As we know, Genetic Algorithm is based on the theory of selection. The solution which sustains the test of survival and has fitness function (traits) to produce new and better solution. GA is so powerful that if used to its efficiency they can program the robot for a surgery, drive a car minus the driver, and many more revolutionary changes which we could only think of. It has some amazing positive outcomes which are making GA trusted and most used technique. It is faster and convenient than other traditional methods of problem-solving. It provides us with not the just good solution but an improvised solution. Also, with good options for solutions, the results will only get better with time.
Apart from being on the high-end technique, it is not suitable for a simple problem where derivative information is readily available.
Since computer coding only understands Binary language, a specific coding language on which all our computers work. Genetic Algorithm mimics the adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. Since the entire theory is based on optimization which is a process of making something better. The process is widely used in-
- Artificial creativity
- Automated designs for mechatronics
- Bioinformatics research
- Climatology-shaping global temperature
- Code-breaking for decryption
- Forensic lab
- Clinical decision in Medicine practice
- Neural networking
- Quality control
- Music record production
- Mechanical and Software Engineering
- Vehicle routing problem
Genetic algorithm applications are proven to be powerful and a robust optimization technique. It has successful application to real-world problems with real solutions. Be it self-driven cars which are our future, the cars have been constantly proving their caliber by withstanding the odds of human and environmental behavior.