1. Overview
In this tutorial, we’ll discuss two crucial steps in a genetic algorithm: crossover and mutation. We’ll explore how crossover and mutation probabilities can impact the performance of a genetic algorithm.
Finally, we’ll present some factors that can help us find optimal values for crossover and mutation.
2. Introduction to Genetic Algorithms
A genetic algorithm is a part of the evolutionary algorithm paradigm and is used to solve complex optimization problems. It’s inspired by natural selection. We can use genetic algorithms to find optimal solutions.
It involves generating a population of possible solutions, evaluating the population, and selecting the best solutions using some operations. We repeat these processes until we find a satisfactory solution to a given problem.