This article presents experiences from the introduction of a new three hour interdisciplinary course on computational intelligence (CI) taught at the Missouri University of Science and Technology, USA at the undergraduate and graduate levels. This course is unique in the sense that it covers five main paradigms of CI and their integration to develop hybrid intelligent systems. The paradigms covered are artificial immune systems (AISs), evolutionary computing (EC), fuzzy systems (FSs), neural networks (NNs) and swarm intelligence (SI). While individual CI paradigms have been applied successfully to solve real-world problems, the current trend is to develop hybrids of these paradigms since no one paradigm is superior to any other for solving all types of problems. In doing so, respective strengths of individual components in a hybrid CI system are capitalized while their weaknesses are eliminated. This CI course is at the introductory level and the objective is to lead students to in-depth courses and specialization in a particular paradigm (AISs, EC, FSs, NNs, SI). The idea of an integrated and interdisciplinary course like this, especially at the undergraduate level, is to expose students to different CI paradigms at an early stage in their degree program and career. The curriculum, assessment, implementation, and impacts of an interdisciplinary CI course are described.


Electrical and Computer Engineering


National Science Foundation (U.S.)

Keywords and Phrases

Course; Graduate; Undergraduate; Computational intelligence

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Feb 2009