Genetic Algorithm, Concepts and Working
Updated on Sep, 2023
Language - English
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
These steps indicated that Genetic Algorithm is a Randomized, search-based optimization Algorithm.
This course is divided into four modules.
- First module – Introduction, history, and terminologies used in Genetic Algorithms.
- 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
What will you learn in this course:
Evolutionary Computation and Genetic Algorithms
Terminologies and operators of Genetic Algorithm
Advanced Operators and Techniques in Genetic Algorithm
Simple Python code for Genetic Algorithm implementation
Applications of Genetic Algorithm
What are the prerequisites for this course?
No prerequisites are there for this course. Students can listen to the lectures to understand Genetic Algorithm concepts from the base.
Check out the detailed breakdown of what’s inside the course
History of Genetic Algorithm
- Introduction to the course on Genetic Algorithm 04:11 04:11
- History of Genetic Algorithm 08:04 08:04
- Terminologies in Genetic Algorithms 09:33 09:33
- Introduction to Genetic Algorithm
Working of Genetic Algorithm
Elements of Genetic Algorithm
Applications of Genetic Algorithms
Dr. DEEBA KANNANDeeba Kannan
Hi!! Im Deeba Kannan, working Assistant Professor in SRM Institute of Science and Technology. I completed my Doctorate in the field of IoT and Deep learning model for Agricultural Enhancement. I love teaching and I am teaching B-Tech students for past 10 years. I do few youtube video lectures for my students in Theory of Computation, Artificial Intelligence and Compiler Design. Im interested more in to the core and base of computer science subjects.
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