Heuristic Search, Hill Climbing and Simulated Annealing

Flashcards covering key concepts from the lecture on heuristic search methods, specifically focusing on hill climbing and simulated annealing.

What is the aim of heuristic search methods?

To find a good solution in a reasonable amount of time, often using a fitness function to score potential solutions.

What does the No Free Lunch Theorem state?

Just because method X is effective for solving problem Y, it may not be effective for solving problem Z, even if Y and Z are similar.

What is the core challenge with identifying the computational complexity of heuristic search methods?

It is often very difficult to define, so performance is usually measured in terms of the number of fitness function calls.

Describe the Random Mutation Hill Climbing (RMHC) algorithm.

An iterative search method that starts at a random point, checks close neighbors for better fitness, and continues from the best one found.

What analogy is used to explain the Random Mutation Hill Climbing algorithm?

A hill walker trying to climb a hill in the fog, feeling around for a nearby direction that goes up.

What is one of the pitfalls of the RMHC algorithm?

It can get stuck in a local optimum and may not reach the global optimum.

What key elements are needed for RMHC Hill Climbing using weights?

A random starting point and a small change to evaluate neighbor solutions.

How does the stochastic hill climbing (SHC) improve upon RMHC?

By allowing the acceptance of worse fitness values to escape local optima.

What is the significance of the temperature parameter in the simulated annealing algorithm?

It regulates the chance of accepting worse solutions, decreasing over time to avoid getting stuck.

What do the results summary indicate about the performance of RMHC and other methods?

RMHC had the best average fitness at 9.0, followed by SHC at 8.0, RRHC at 6.2, and SA at 5.8.

What is a defining characteristic of the search methods used in HC and SA algorithms?

They are local or neighbourhood search methods, examining neighboring points for optimal solutions.