Algoritmos genéticos | | UPV

Title: Genetic Algorithms Description: Alberola Oltra, Juan Miguel; This video presents an introduction to genetic algorithms, explaining their generic structure and application. http://hdl.handle.net/10251/194272 Automatic description: In this video, the professor introduces genetic algorithms, an optimization technique inspired by natural evolution. He uses the knapsack problem, a classic optimization technique where profit must be maximized by selecting weight-restricted packages, to explain how these algorithms work. He emphasizes that, although genetic algorithms do not guarantee the optimal solution, they are useful for obtaining good solutions in reasonable times. He explains the main features of genetic algorithms, including the representation of solutions and the utility function for evaluating them. It then addresses the evolutionary process of algorithms, which begins with an initial population of solutions (chromosomes) and undergoes genetic operations such as crossover and mutation to evolve toward the best possible solution. It shows an example of a binary representation of solutions for the knapsack problem and how their utility is defined. The iterative process, it explains, involves generating a random population of solutions, evaluating their utility, and applying genetic techniques that allow the best parents to be chosen, in order to produce offspring that could lead to the optimal solution. Individuals are selected for the next generation until the desired solution is reached or up to a defined limit of iterations. It concludes by emphasizing the importance of a good representation of solutions and the appropriate choice of utility function. These preliminary steps are crucial to the success of the genetic algorithm, which relies on an iterative process of crossover and mutation, and the selection of the best individuals for each generation. Author: Alberola Oltra Juan Miguel Polytechnic University of Valencia (UPV): https://www.upv.es More videos at:    / valenciaupv   Access our MOOCs: https://upvx.es #Optimization Methods #Artificial Intelligence #Genetic Algorithms #Optimization #