Genetic Algorithm Concepts and Working

Genetic Algorithm Concepts and Working

Genetic Algorithm is a search based optimization algorithm used to solve problems were traditional methods fails. It is an randomized algorithm where each step follows randomization principle.

Genetic Algorithm was developed by John Holland, from the University of Michigan, in 1960. He proposed this algorithm based on the Charles Darwin’s theory on Evolution of organism. Genetic Algorithm follows the principal of “Survival of Fittest”. Only the fittest individual has the possibility to survive to the next generation and hence when the generations evolve only the fittest individuals survive.

Genetic Algorithms operates on Solutions, hence called as search based optimization algorithm. It search for an optimal solution from the existing set of solutions in search space. The process of Genetic Algorithm is given as,

1. Randomly choose some individuals (Solutions) from the existing population

2. Calculate the fitness function

3. Choose the fittest individuals as parental chromosomes

4. Perform crossover (Recombination)

5. Perform Mutation

6. Repeat this process until the termination condition

This steps indicated that Genetic Algorithm is an Randomized, search based optimization Algorithm.

This course is divided into four modules.

First module – Introduction, history and terminologies used in Genetic Algorithm.

Second Module – Working of genetic algorithm with an example

Third Module – Types of Encoding, Selection, Crossover and Mutation methods

Fourth module – Coding and Applications of Genetic Algorithm

Happy Learning!!!