PowerPoint Presentation

PRESENTED BY POOJA AR. TOPIC: HILL CLIMBING.

abstract. HILL CLIMBING. Hill climbing is a simple optimization algorithm used in Artificial Intelligence (AI) to find the best possible solution for a given problem. It belongs to the family of local search algorithms and is often used in optimization problems where the goal is to find the best solution from a set of possible solutions..

abstract. )bjectist function shoulder Global maximum Local maximum "flat" local maximum State ('urrent state.

abstract. There are several variations of Hill Climbing •Simple hill climbing •Steepest ascent Hill Climbing • Stochastic hill climbing.

abstract. State space daigram for Hill Climbing: •The state-space diagram is a graphical representation of the set of states our search algorithm can reach vs the value of our objective function(the function which we wish to maximize) •The best solution will be a state space where the objective function has a maximum value(global maximum)..

abstract. slwuldd maumum "flat". X-axis: denotes the state space ie states or configuration our algorithm may reach. Y-axis: denotes the values of objective function corresponding to a particular state..

abstract. Advantages of Hill Climbing algorithm: 1. Hill Climbing is a simple and intuitive algorithm that is easy to understand and implement. 2. It can be used in a wide variety of optimization problems, including those with a large search space and complex constraints. 3. Hill Climbing is often very efficient in finding local optima, making it a good choice for problems where a good solution is needed quickly..

abstract. Disadvantages of algorithm: Hill Climbing 1. Hill Climbing can get stuck in local optima. 2. The algorithm is sensitive to the choice of initial solution 3. Hill Climbing does not explore the search space very thoroughly, which can limit its ability to find better solutions..