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CS 1 – Introduction to Computer Science

Dive into the fascinating realm of evolutionary computing and learn to design innovative algorithms for solving complex real-world problems. Uncover research challenges and current projects in this cutting-edge field.

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CS 1 – Introduction to Computer Science

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  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 • CS128 Discrete Mathematics • CS347 Introduction to Artificial Intelligence • CS348 Evolutionary Computing • CS448 Advanced Evolutionary Computing

  3. CS128 – 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. Design & Application of Novel Evolutionary Algorithms for Real-World Problem Solving • 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

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