The greedy method is one of the most commonly used techniques in algorithm design and there are many different optimizations available.
This thesis presents one of them.
Our idea for optimization was improving the running time of greedy algorithms with the help of the quicksort algorithm.
We integrated the greedy algorithm into quicksort to produce a hybrid algorithm, which can solve certain problems significantly faster than the normal greedy algorithm.
In the thesis we chose to test hybridization on the activity-selection problem, the fractional knapsack problem, the coin changing problem, Kruskal's algorithm and unit-task scheduling.
We experimentally confirmed that hybrid algorithms (do) indeed perform better with the coin changing problem and the fractional knapsack problem.