Metaheuristics are search optimisation techniques based on heuristic algorithms that are used to solve complex problems where the search space is very large or unknown. Metaheuristics are general-purpose algorithms that are not designed for a specific problem, but can be adapted to a wide variety of optimisation problems in different fields, such as artificial intelligence, engineering, economics, biology and many others. Metaheuristics are algorithms that rely on iterative exploration of the solution space, using strategies to move intelligently through the search space and to escape local optima. Some of the best known metaheuristics are the genetic algorithm, the ant colony algorithm, simulated annealing, particle swarming and tabu search optimisation. Metaheuristics are used in artificial intelligence and machine learning applications for hyperparameter optimisation, feature selection and neural network optimisation, among other tasks.
Artificial Intelligence (AI) derives from a series of models or branches that can be used in different areas of people's lives, as well as in different areas of [...]
Read More »If we look at them separately, the Internet of Things (IoT) and Artificial Intelligence (AI) are powerful technologies and if we combine them, we get a [...]
Read More »If you don't know the difference between an ERP (Enterprise Resource Planning) system and a CRM (Customer Relationship Management) system, here's what you need to know about the [...]
Read More »The first thing you need to know is the limits of AI and after mastering the basic concepts you will be able to build a large commercial software with intelligent [...]
Read More »
