Metaheuristics for Machine Learning

Call for Papers Metaheuristic optimization algorithms are essentially stochastic in nature and can be applied to any optimization problem, independent of whether the problem is formulated as a monoobjective or as a multiobjective optimization problem. They are called nature inspired algorithms because their origin comes from the observation of natural behavior: the analogies can be drawn from the fields of physics (simulated annealing, microcanonical annealing, etc.), or biology (evolutionary algorithms) or ethology (ant colonies, particle swarms, etc.). Algorithms, techniques, and methods based on metaheuristic paradigm have been successfully applied to a wide range of complex problems. From the perspective of development of science, metaheuristics are an emerging interdisciplinary area combining diverse domains like natural sciences, biology, sociology, and computer science. Their rapid growth is a natural consequence of the rapidā€¦


Link to Full Article: Metaheuristics for Machine Learning