Development of a Computational Intelligence Course for Undergraduate and Graduate Students

This paper presents the design, implementation and experiences of a new three hour experimental course taught for a joint undergraduate and graduate class at the University of Missouri-Rolla, USA. The course covers the four main paradigms of Computational Intelligence (CI) and their integration to develop hybrid systems. The paradigms covered are artificial neural networks (ANNs), evolutionary computing (EC), swarm intelligence (SI) and fuzzy systems (FS). While individual techniques from these CI paradigms have been applied successfully to solve real-world problems, the current trend is to develop hybrids of paradigms, since no one paradigm is superior to the others in all situations. In doing so, we are able capitalize on the respective strengths of the components of the hybrid CI system and eliminate weakness of individual components. This course is an introductory level course and will lead students to courses focused in depth in a particular paradigm (ANNs, EC, FS, SI). The idea of an integrated course like this is to expose students to different CI paradigms at early stage in their degree program. The paper presents the course curriculum, tools used in teaching the course and how the assessments of the students’ learning were carried out in this course. Introduction A major thrust in the algorithmic development and enhancement is the design of algorithmic models to solve increasingly complex problems and in an efficient manner. Enormous successes have been achieved through modeling of biological and natural intelligence, resulting in “intelligent systems”. These intelligent algorithms include neural networks, evolutionary computing, swarm intelligence, and fuzzy systems. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms form part of the field of Artificial Intelligence (AI) [1]. Just looking at this wide variety of AI techniques, AI can be seen as a combination of several research disciplines, for example, engineering, computer science, philosophy, sociology and biology. There are many definitions to intelligence. The author prefers the definition from [1] Intelligence can be defined as the ability to comprehend, to understand and profit from experience, to interpret intelligence, having the capacity for thought and reason (especially, to a higher degree). Other keywords that describe aspects of intelligence include creativity, skill, consciousness, emotion and intuition. Computational Intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include those AI paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The four dominant computational intelligence paradigms are neural networks, evolutionary computing, swarm intelligence and fuzzy systems as illustrated in figure 1. Evolutionary Computing Neural Networks Fuzzy Systems Swarm Intelligence Neuro-Fuzzy Systems Neuro-Genetic Systems Neuro-Swarm Systems