GA – Genetic Algorithm


What is Genetic Algorithm (GA)?

Genetic Algorithm is one type of computational technique. It is abbreviated as GA.

 In genetics, Evaluation is the most important factor. During evaluation, two chromosomes combine together and share the genetic information. After sharing, they part off and create new creatures. Genomes help to transfer this information from generation to next generation.

The transformation or acceptance of information depends upon the survival of the fit-test. The genes which have the capability for survival of the fit-test are the reasons for the formation of creatures suitable to the surroundings.

Basics of Genetic Algorithm in mathematical terms:

Consider a problem – It might have:

  • Different solutions to solve it and
  • Different procedures to solve it.

“Integrating the different solutions and finding many good solutions” is the Basis of Genetic Algorithm. The solutions obtained through this way serve the best and looks simpler when compared with the others.

For example, consider a problem P. Let W, X, Y, Z be the different solutions for this problem. 
 


Fig -A

Now, the combination of any two solutions can give a better solution than WXYZ. For example, combining W and X or else with some other solution may give some new best solutions A and B.


 
Fig -B

These mathematical combinations may be also written in BINARY form, which helps in Genetic algorithm. To write in Binary form, the unique solutions are taken as binary codes.
 


Fig -C

During this transformation, good matters are alone taken and a net result with the best matters is obtained. So, the new solutions will be the very best solutions. Similarly, combining the mathematical format with the genetics can give Genetic Algorithm.

Basics of Genetic Algorithm in Genetic terms:

In the previously explained method, some famous genetic words are COMBINED WITH mathematical algorithm in a particular form. Combining some words from genetics, to Genetic Algorithm helps to understand the Genetic computational method. The words are:

  • Parents
  • Cross over of Chromosomes
  • Hereditary Gene
  • Mutation

Now, consider two people as two solutions. As like combining two solutions, the two people are combined together by cross over. The people who combine can be taken as “Parents”.

During cross-over, two different procedures called as, Chromosomes from two different people combine together and creates new procedures. These new procedures can be called by the name “Children”. 
 

As like fig. A, the genetic solution can be redrawn as:

Fig -D

Particularly, the higher qualities and unique characters of the Parents will be within the new procedures (Children). Genes takes these qualities from a parent to children. Because of this, the heredity will be safe guarded.

Now, consider fig C. From fig C, each and every single bit in the binary code can be taken as individual gene.

During this transformation through gene, good matters are alone taken and a net result with the best matters is obtained. So, the new solutions will be the very best solutions.
The chromosomes or strings turns to be the best one because of this evaluation function.

 
Fig -E

Types of Genetic Algorithm:

In the genetic algorithm computational technique, there are two types:

  • In the first type, the fractions are kept at the same place by some other different fractions. It is a simplest type.
  • In the second type, some of the fractions are changed in many different fractions.

Applications of GA:

It is used in recent trends like:

  • VLSI
  • Image Processing and,
  • In all places where the best solution needs to be found out.

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