Home
Please refer to the example section for an in depth tutorial.
| Example | Concept |
|---|---|
| Minimize-Mathematical-Function-Rastrigin-Part-1 | Introduction. Solving numerical problems. Creating a fitness function. |
| Minimize-Mathematical-Function-Rastrigin-Part-1 | Introduction. Solving numerical problems. Creating a fitness function. |
| Minimize-Mathematical-Function-Rosenbrock | Multi threading, migration and multi threading |
| Charting-,-Visualization-and-result-listener | Using result listeners to access data during computation, display charts showing the ga's output. Save results to file for later usage |
| Custom-categorical-problems | Implementing custom non numerical problems |
- Fitness function: the main objective of the genetic algorithm is to minimize a given function, otherwise known as objective function. The fitness value of an individual indicates how good of a solution the given answer represents
- Individual: a solution to the given problem. An individual holds a unique set of variables which can be applied to the fitness function to compute a value
- Population: A collection of (solution) individuals
- Generation: Due to the itterative nature of genetic algorithms a single population
- Search Space:
- Diversity: How similar individuals in a population are. As the generation count progresses the populations are bound (and expected) to diverge to a solution. Keeping a healthy share of distinct individuals in the population prevents the algorithm to get stuck in local minima. A diverse population features individuals with many different traits.
- Exploration:
- Exploition:
[1] https://se.mathworks.com/help/gads/what-is-the-genetic-algorithm.html