On Methods for Genetic Algorithms
A detailed overview of Genetic Algorithm (GA) methods based on a literature review, illustrating key concepts such as selection, crossover, mutation, replacement, and termination. Using a custom visual language based on RGB fitness values, the memo explains common GA techniques like Roulette Wheel Selection, Tournament Selection, Single- and Multi-Point Crossover, and various mutation strategies. Mathematical formulas and clear diagrams accompany the explanations, providing a solid foundation for understanding and applying GAs in optimization problems.