Genetic Algorithm Concepts and Working
Genetic Algorithm Concepts and Working
What you will learn
Explore the principles of Evolutionary Computation and delve into Genetic Algorithms.
Familiarize with the key terminologies and operators essential for Genetic Algorithm operation.
Advance understanding through the exploration of sophisticated operators and techniques within Genetic Algorithms.
Acquire practical skills by implementing Genetic Algorithms through simple Python code.
Discover real-world applications where Genetic Algorithms offer effective solutions.
Why take this course?
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!!!