Welcome to this beginner-friendly introduction to Genetic Algorithms (GA)—a powerful optimization technique inspired by natural selection and evolution. In this video, you'll learn the basic concepts, components, and working process of genetic algorithms with simple examples and visuals.

Perfect for students, AI enthusiasts, and anyone curious about how computers solve complex problems using evolutionary strategies.

What You’ll Learn:

What is a Genetic Algorithm?

Key components: Population, Fitness, Selection, Crossover, Mutation

How GA mimics natural evolution

Real-world applications

Simple example or demo (if included)

Subscribe to Lecturelia for more clear and engaging tutorials on algorithms, AI, and computer science!

#GeneticAlgorithm #GATutorial #EvolutionaryAlgorithms #AI #MachineLearning #Optimization #ComputerScience #Lecturelia #ArtificialIntelligence #AlgorithmTutorials
#EvolutionaryAlgortihm
#GeneticAlgorithm
#ArtificialIntelligence
#ai
#asadnomanee #bangla #CSE
#lecturelia #tutorial
#full
#complete
#playlist
#courses
#mbstu
#mbstu_cse
#lecture
#lectures
#tutorials
#playlists
#course


The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.

This flow chart outlines the main algorithmic steps.