savprogram 0.0.1

A library with different Machine Learning Algorithms: Hill Climbing, Simulated Annealing, Brute Force, and A star search

This program includes Machine Learning Algorithms: Hill Climbing, Simulated Annealing, Brute Force and A*Search.

Hill Climbing Hill climibing is a widely used optimization algorithm in Artificial Intelligence that helps find the best possible solution to a given problem. As part of the local search algorithms family, it is often applied to optimization problems where the goal is to idenitfy the optimal solution from a set of potential candidates.

Simulated Annealing Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization problem. For large numbers of optima, SA can find the global optimum. It is often used when the search space is discrete. For finding problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be prederable to exact algorithms such as gradient descent or branch and bound.

Brute Force Brute force algorithm is a simple, comprehensive search strategy that systematically explores every option until a problem’s answer is discovered. It’s a generic approach to problem-solving that’s employed when the issue is small enough to make an in-depth investigation possible. However, because of their temporal complexity, brute force techniques are inefficient for large-scale issues.

A*Search A*Search algorithm is one of the best and popular technique used in path-finding and graph traversals. Informally speaking, A*search algorithms, unlike other traversal techniques, it has “brains”. What it means is that it is really a smart algorithm which separates it from the other conventional algorithms.