What does metaheuristics mean, and what machine learning approaches are there that can be used metaheuristically?
Metaheuristics is a broad term referring to any optimization technique which uses heuristics or other non-optimal solutions in the search for an optimal solution. There are various types of metaheuristics including genetic algorithms (GAs), simulated annealing, memetic algorithms, and tabu search.

In machine learning, metaheuristic algorithms can be applied as a component in a broader approach known as evolutionary computation, which also includes techniques such as random walks, tournament selection, and particle Swarm Optimization. In this context, metaheuristic algorithms can be used to explore the solution space, identify regions containing promising solutions, and guide the optimization process towards finding the best possible result. For example, in image generation tasks using deep neural networks, a GAA could be employed to generate diverse images within specified constraints, while a PSO could be used to evolve the weights of the network over time.

It's important to note that the use of metaheuristics alone may not guarantee the success of an AI system, particularly when dealing with complex problems where finding the global optimum is challenging. However, they can provide useful guidance and help to improve the overall effectiveness of the algorithm by enabling it to find more efficient local optimizations.