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Updated on Sep, 2026
Programming, Data Science, Artificial Intelligence
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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)
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.
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
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
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