Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field.

In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.

In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.