## heuristics

##### AM-76 - Simulated annealing • Outline the rationale for and usefulness of simulated annealing
##### AM-75 - Interchange with probability • Explain how the process to break out local optima can be based on a probability function
• Outline the TABU heuristic
##### AM-74 - Interchange heuristics • Define alternatives to the Tietz and Bart heuristic
• Outline the Tietz and Bart interchange heuristic
• Describe the process whereby an element within a random solution is exchanged, and if it improves the solution, it is accepted, and if not, it is rejected and another element is tried until no improvement occurs in the objective function value
##### AM-73 - Greedy heuristics • Demonstrate how to implement a greedy heuristic process
• Identify problems for which the greedy heuristic also produces the optimal solution (e.g., Kruskal’s algorithm for minimum spanning tree, the fractional Knapsack problem)
##### AM-74 - Interchange heuristics • Define alternatives to the Tietz and Bart heuristic
• Outline the Tietz and Bart interchange heuristic
• Describe the process whereby an element within a random solution is exchanged, and if it improves the solution, it is accepted, and if not, it is rejected and another element is tried until no improvement occurs in the objective function value
##### AM-75 - Interchange with probability • Explain how the process to break out local optima can be based on a probability function
• Outline the TABU heuristic
##### AM-76 - Simulated annealing • Outline the rationale for and usefulness of simulated annealing
##### AM-73 - Greedy heuristics • Demonstrate how to implement a greedy heuristic process
• Identify problems for which the greedy heuristic also produces the optimal solution (e.g., Kruskal’s algorithm for minimum spanning tree, the fractional Knapsack problem)
##### AM-76 - Simulated annealing • Outline the rationale for and usefulness of simulated annealing
##### AM-75 - Interchange with probability • Explain how the process to break out local optima can be based on a probability function
• Outline the TABU heuristic