What do we learn in class :

In this week we learned about two main topics, first is informed search and second is Local Search.

Informed Search:

First, we talked about each data contains heuristic h(n), with that, we discuss the main section which are Best first search, Greedy search, and A* search.

To sum up briefly:

Best first search tries to expand most desirable
unexpanded nodes.

Greedy search is the search that tries to prioritize the smallest heuristics among all the paths to find the destination of a node, it is very fast but not optimize

A* search is a search that tried to find all the possibility of paths and choose the smallest edge to continue to find another possibility of paths, it is not as fast as Greedy search but its more optimize.

Local Search :

Hill-climbing, Complete Search, TSP and Genetic Algorithm.

Hill-climbing is basically tried to find a good solution for one algorithm by seeing the complexity analysis.

The complete search indicated that every node is a solution, the operators go from one solution to another, it can stop any time and have a valid solution and same as hill-climbing search is now about finding a better solution.

TSP is basically to find out the shortest path from starting at point n to the destination ( the starting point) by travelling each of nodes at least ones.

And Genetic algorithm is kinda related to Biology, it’s a metaheuristic that related to natural selection. I think the Genetic algorithm was the hardest for me since the lecture said it was related to Biology and I am not good at Biology.

My obstacles and challenges:

At that day I did not feel very well so I could not concentrate on the class really well so it is very hard for me to understand all the types of search at once especially local searches. The second exercise is also very hard since you need to go over the slides, again and again, to look for solutions, but for now, I only see what my friends did just in case I could get some ideas from them.