Lecture 24 iterative improvement algorithm
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Iterative improvement algorithms are suitable for optimization problems where the path to a solution is irrelevant and the goal state itself is the solution. These algorithms maintain a single current state and try to iteratively improve it. Some key advantages are that they only require tracking the current state, saving memory, and are suitable for both online and offline search. Examples of problems that can use iterative improvement algorithms include the traveling salesman problem and N-Queens problem.
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Lecture 24 iterative improvement algorithm
- 1. Iterative Improvement Algorithms Lecture-24 Hema Kashyap 1
- 2. Introduction • In many optimization problems, path is irrelevant, the goal state itself is solution. Eg. TSP, N-Queens Problem • In such cases , one can use iterative improvement algorithms • Keeping a single current state, try to improve it. 2
- 3. • For the most practical approach in which • All the information needed for a solution are contained in the state description itself • The path of reaching a solution is not important • Advantage: memory save by keeping track of only the current state 3
- 4. • When path is irrevant and goal state itself is the solution • Then state space = a set of goal states – find one that satises constraints (e.g., no two classes at same time) – or, nd optimal one (e.g., highest possible value, least possible cost) • In such cases, can use iterative improvement algorithms; keep a single current" state, try to improve it – Constant space – Suitable for online as well as oine search 4
- 7. Classes of Itreative improvement algorithms • Hill Climbing (gradient decent) • Local Beam Search • Simulated Annealing • Genetic Algorithm 7