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Genetic Algorithm In Hindi ~xRay Pixy

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Transient Search Optimization Algorithm || Step-By-Step || ~xRay Pixy
https://youtu.be/T2lVQ8mYFoM
Video Chapters: TSO Algorithm
00:00 Introduction
00:44 Topics Covered
01:14 Transient Behavior
02:57 Transient Search Optimization Algorithm
06:10 TSOA Mathematical Models
10:30 TSOA Step-By-Step
15:32 TSOA Applications
15:58 TSOA Advantages
16:22 TSOA Disadvantages
16:28 Conclusion

Evolutionary algorithms (EAs) are optimization methods inspired by the process of evolution in nature. They aim to find the best solutions to problems by mimicking natural selection and genetics.

Key Steps in Evolutionary Algorithms:

  1. Start with a Population:

    • Think of a population as a group of random guesses or potential solutions to your problem.
    • Each "individual" in the population represents one solution.

  2. Evaluate Fitness:

    • Just like in nature, some individuals are better suited to survive in their environment.
    • In EAs, the "fitness" of a solution tells us how good it is at solving the problem.

  3. Select the Best:

    • The fittest individuals are selected to create the next generation. This ensures that the best traits are passed on.

  4. Crossover (Reproduction):

    • Combine parts of two parent solutions to create new solutions (offspring).
    • This is like mixing genes in biology to get new traits in children.

  5. Mutation:

    • Randomly tweak some solutions to introduce variety.
    • This prevents the algorithm from getting stuck in one spot and helps explore new possibilities.

  6. Replace and Repeat:

    • The weakest solutions are replaced by the new ones.
    • The process is repeated for many generations until the best solution is found.

Examples of Evolutionary Algorithms:

  • Genetic Algorithm (GA): Uses crossover and mutation heavily to evolve solutions.
  • Evolution Strategy (ES): Focuses on tweaking individuals more gradually.
  • Genetic Programming (GP): Evolves entire programs or decision trees.
Genetic algorithm (GA) is a method used in computer science to solve complex problems. It works by copying how nature evolves living things, using ideas like natural selection, reproduction, and mutation.

How it works:

  • Start with random guesses: The algorithm creates a bunch of random solutions (like guesses) to the problem.
  • Choose the best ones: It picks the solutions that work better (similar to survival of the fittest).
  • Combine them: The good solutions are "mixed" together to make new ones.
  • Add variety: Small random changes (mutations) are added to explore more possibilities.
  • Repeat: Over time, the solutions get better and better.

#optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
#optimizationproblem #optimizationalgorithms 

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