CS 471 - Optimization

Description:
Unimodal and Multimodal problems; combinatorial optimization problems; deterministic algorithms; Hill climbing algorithm; Tabu Search Algorithm; Evolutionary algorithms; Particle swarm optimization; Differential evolution; Single and Mutli-objective optimization.

Prerequisites:
Prerequistes: CS 302 and CS 325 and MATH 330.

Credits: (4)

Learner Outcomes, Activities and Assessments

Demonstrate knowledge of different problem domains.	Students will work individually on different problem domain classifications, such at unimodel, multimodel, combinatorial, discrete and mixed.	Written examination on attained knowledge and part of project definition.
Differentiate different optimization algorithm classification, in terms of deterministic and stochastic algorithms.	Students will learn to identify different algorithms and its appropriate usage	Graded examination.
Formulate deterministic algorithms using given set of rules on a specified problem domain.	Students will learn to code a given set of deterministic algorithms and test its performance on a predefined multimodal problem set.	Faculty mentor and student will discuss its feasibility and level of performance.
Formulate stochastic algorithms based on population topology.	Students will code an individual stochastic algorithm on a predefined combinatorial problem set.	Graded project and presentation.
Assess the performance characteristics of different algorithms through benchmark tests.	Students will learn to compute different benchmark tests in order to test the performance of different algorithms.	Graded submission as part of project.
Evaluate the suitability of different optimization algorithms in given scenarios.	Students will learn to distinguish based on problem definition, as to which algorithms can be used most successfully and in which scenario.	Faculty mentor and student will discuss the scenarios and outcomes.