Скачать или смотреть (Lec-105) Crossover and Mutation in Genetic Algorithms - Part 2 | AI বাংলা টিউটোরিয়াল

In this video, we break down the key genetic operators—Crossover and Mutation—used in Genetic Algorithms to generate new solutions and maintain diversity in the population. Learn how these processes mimic biological evolution to solve complex optimization problems.

📌 What you'll learn:

What is Crossover?

What is Mutation?

Different types (single-point, two-point, uniform crossover; bit flip, swap mutation, etc.)

Their role in evolutionary algorithms

Step-by-step examples and visuals

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Crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. Crossover is sexual reproduction. Two strings are picked from the mating pool at random to crossover in order to produce superior offspring. The method chosen depends on the Encoding Method.

Different types of crossover :
Single Point Crossover : A crossover point on the parent organism string is selected. All data beyond that point in the organism string is swapped between the two parent organisms. Strings are characterized by Positional Bias.

Two-Point Crossover : This is a specific case of a N-point Crossover technique. Two random points are chosen on the individual chromosomes (strings) and the genetic material is exchanged at these points.

Uniform Crossover : Each gene (bit) is selected randomly from one of the corresponding genes of the parent chromosomes.
Use tossing of a coin as an example technique.

The crossover between two good solutions may not always yield a better or as good a solution. Since parents are good, the probability of the child being good is high. If offspring is not good (poor solution), it will be removed in the next iteration during “Selection”.