1. CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Daniel Tauritz, Ph.D. Associate Professor of Computer Science

2. Teaching CS158 Discrete Mathematics CS347 Introduction to Artificial Intelligence CS348 Evolutionary Computing CS448 Advanced Evolutionary Computing

3. CS158 – Discrete Mathematics The mathematical foundations for creating discrete abstractions of the real-world and algorithms to operate on those abstract structures.

4. CS347 – Introduction to AI Problem solving through state space search (search algorithms which operate on abstract representations of the real-world) AI Tournament

5. CS348 – Evolutionary Computing Problem solving through stochastic, population-based search inspired by natural evolution theory (algorithms which operate on abstract representations of the real- world)

6. CS448 – Advanced Evolutionary Computing Individual research projects The goal of scientific research is to add to the body of knowledge

7. Evolutionary Algorithm (EA) Research Challenges  How to design user-friendly EAs?  How to prevent premature convergence?  How to efficiently identify high-quality strategy parameters?  How to prove convergence to exactly, or within ε of, the global optimum?  How to prevent cycling, disengagement, and mediocre stability in CoEAs?  How to overcome the curse of dimensionality in evolutionary computing?  How to compute objective fitness values in CoEAs? Current Research Projects  Parameterless Evolutionary Algorithms  Coevolutionary Automated Software Correction  Critical Infrastructure Protection via Computational Arms-Races  Inverse Diffusion Analysis Employing Genetic Programming  Deriving Historical Information from Dynamic, Diffusive, Environmental Systems  Autonomous Evolutionary Algorithms  Co-Optimization  Evolutionary Rule-Based Intrusion Detection Systems Sample Application Areas  Black Box Optimization  Combinatorial Problem Solving  Configuration Optimization  Modeling / System Identification  Automated Problem Solving  Automated Software Engineering  Co-Learning / Optimization  Simulating Natural Evolution Design & Application of Novel Evolutionary Algorithms for Real-World Problem Solving