02 - Randomized Optimization

What is an Optimization Problem?

An optimization problem is defined by Russell and Norvig (2010) as a problem in which “the aim is to find the best state according to an objective function.”

Optimization Algorithms

1. Hill climbing
2. Randomized hill-climbing
3. Simulated annealing
4. Genetic algorithms
5. Neural Network

Problems

1. Knapsack
2. Travelling Salesperson
3. N-Queens
4. Max-K Color optimization
5. Flip Flop
6. Continuous Peaks

Instructions

1. Go to .
2. Click on "Code"
3. Click on "Download ZIP"
4. Unzip the files
5. Run each of the Python files individually.

Results

1. All results will be printed to the console including scores and execution times.
2. All the graphs will be generated directly in the repository once the files are run successfully.