Simulated Annealing (SA) is a probabilistic technique used for finding an approximate solution to an optimization problem. To find the optimal solution when the search space is large and we search through an enormous number of possible solutions the task can be incredibly difficult, often impossible. Even with today’s modern computing power, there are still often too many possible solutions to consider. Therefore, we often settle for something that’s close enough to optimal solution when we may be not able to find the optimal solution within a certain time.
Lately, I have been working on artificial intelligence. I have read the article of “Mastering the Game of Go with Deep Neural Networks and Tree Search Research” introduces how to combine the best of deep neural networks and search algorithms to achieve what was thought the impossible.
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence because of its enormous search space and the difficulty of evaluating board positions and moves.