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Using GADEMO for Function Optimization
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GADEMO can be used to solve a variety of optimization problems by leveraging benchmark functions commonly used in the Genetic Algorithms literature.

To illustrate its full potential, a comprehensive set of test cases and execution results are presented in `Chapter 8 of the final project's paper <https://www.maxwell.vrac.puc-rio.br/68870/68870.PDF>`_.

Specifically, **Section 8 — "Resultados e Discussão"** covers a wide range of experiments including:

- **Crossover and Mutation Rates**
- **Population Size and Linear Normalization**
- **Comparison of Crossover Operators**
- **Elitism Strategies**
- **Steady-State vs. Generational Models**

These experiments were applied to a set of benchmark functions widely used in the optimization field, such as:

- **F6**
- **Rastrigin**
- **Ackley**
- **Levy**
- **Drop-Wave**

Each example includes insights about the impact of parameter tuning, diversity preservation, convergence patterns, and solution quality. The results are thoroughly discussed using the graphical outputs generated by GADemo, offering users both theoretical understanding and practical feedback.

These examples serve as a valuable reference for students, researchers, and developers interested in learning more about GADemo’s capabilities and how it performs under various optimization scenarios.