3 Credit Hour Course
Intended For Level 0 Term 0 Students
Prerequisite:
Heuristics and meta-heuristic: notation, motivations, applications; Representations: vectors, graphs, trees, lists, rulesets; Single-state methods: hill-climbing, global optimization algorithms, simulated annealing, tabu search, iterated local search, guided local search, reactive local search, greedy randomized adaptive search procedures; Nature inspired methods: evolution strategies, genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, artificial immune systems; Hybrid methods; Parallel methods: multiple threads, island models, master-slave fitness assessment, spatially embedded models; Multiobjective optimization; Performance evaluation.
