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The Nature of Code style implementation of Rockets evolving using Genetic Algorithm in Java

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Rocket Evolution 🚀🔥

About 🗣️

This project simulates the evolution of rockets using genetic algorithms. The goal is to demonstrate the concept of survival of the fittest, where the fittest rockets are selected to reproduce and create new offspring. The genetic algorithm is used to evolve the rockets over time, resulting in a population of rockets that are better adapted to their environment.

Concept 📚

The genetic algorithm is a type of optimization technique inspired by the process of natural selection. It works by creating a population of candidate solutions, evaluating their fitness, and selecting the fittest individuals to reproduce and create new offspring. The process is repeated over multiple generations, resulting in a population of solutions that are better adapted to the problem.

A genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

It is inspired by Charles Darwin's theory of Natural Selection.

Survival Of The Fittest.

How it works 🤔

  1. Initialization: A population of rockets is created with random parameters.
  2. Evaluation: The fitness of each rocket is evaluated based on its performance.
  3. Selection: The fittest rockets are selected to reproduce and create new offspring.
  4. Crossover: The selected rockets are combined to create new offspring.
  5. Mutation: The new offspring are mutated to introduce random changes.
  6. Replacement: The least fit rockets are replaced with the new offspring.
  7. Repeat: The process is repeated over multiple generations.

Features 🎉

  • Simulation of rocket evolution using genetic algorithms
  • Survival of the fittest concept
  • Genetic algorithm optimization technique
  • Multiple generations of rockets
  • Random mutations and crossover

Getting started 🚀

  1. Clone the repository: git clone https://github.com/VinayHajare/RocketEvolution.git
  2. Run the simulation: java -jar RocketEvolution.jar
  3. Observe the evolution of the rockets over time.

Contributing 🤝

Contributions are welcome! If you have any ideas or suggestions, please open an issue or submit a pull request.

License 📝

This project is licensed under the MIT License. See the LICENSE file for details.

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The Nature of Code style implementation of Rockets evolving using Genetic Algorithm in Java

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