Computational Intelligence (CI) techniques are essential to solve many of the real-world problems found in today’s data-driven decision making, where traditional analytical methods fall short due their complexity. You will learn the fundamental concepts in CI, exploring how CI algorithms can efficiently navigate solution spaces to find (near-) optimal solutions to complex problems. You will develop skills in applying Genetic Algorithms and Genetic Programming to solve complex problems in Business, Economics, and Finance through hands-on exercises and case studies. You will learn to identify and analyse problem domains suitable for Genetic Algorithms and Genetic Programming, as well as how to implement and evaluate these algorithms effectively.
Total Contact Hours (Lectures, Classes) 32
Mini-project worth 50%.
Computer-Based, Invigilated Mini Practical worth 50%.
Reassessment Method: Like-for-like Including composite form of reassessment for failed components – Online test
On successfully completing the module, students will be able to:
Critically evaluate Computational Intelligence techniques and their relationship to complex problems.
Appraise and analyse real-world problems from the fields of Business, Economics and Finance.
Propose Genetic Algorithms and Genetic Programming algorithms to solve a given business problem.
Critically evaluate solutions to real-world complex problems.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.