CS 571: Evolutionary Computation
Instructor: Jugal Kalita
Relevant Links
Class Material
Lectures
- Lecture 1 (1/18): Discussion of syllabus, grading scheme, etc.
- Lecture 2 (1/23): Optimization from Chapra and Canale's Numerical Methods for Engineers book. Chapters 13: 1-D Unconstrained Optimization, Chapter 14: Multidimensional Unconstrained Optimzation, Chapter 15: Constrained Optimization
- Lecture 3 (1/25): Local search algorithms from Russell and Norvig's AI book. Chapter 4: Section 4.1: Local Search Algorithms and Optimizaton Problems.
- Lecture 4 (1/30): Protein function, Cell Structure: a video also ; Chromosomes ; Human chromosomes ; Lengths of Human Chromosomes ; Cell division: Mitosis: a video also; and Meiosis. A presentation on evolution from the University of California, Berkeley.
- Lecture 5 (2/1): Introduction to basic genetic algorithms. Chapter 2: The Binary Genetic Algorithm from "Practical Genetic Algorithms", 2nd edition by Haupt and Haupt. Chapter 3 of Sivandandan and Deepa covers the same material, but in a much cryptic way.
- Lecture 6 (2/6): We cover Chapter 3: The Continuous Genetic Algorithm from Haupt and Haupt. There is a good survey of genetic operators for real-valued genetic algorithms in MS thesis by A.A. Adewuya at MIT (1996).
- Lecture 7 (2/8): We discussed the paper "Genetic algorithms for the traveling salesman problem" by Jean-Yves Potvin, Annals of Operations Research, Vol. 63, 1996, 339-370.
- Lectures 8 and 9 (2/13, 2/15): Midterm presentations. Discussion of the paper titled "Designing Soft Keyboards for Brahmic Scripts" by Hinkle, Lezcano and Kalita, Proceedings of 8th International Conference on Natural Language Processing, ICON-2010, Kharagpur, India. What are Brahmic scripts? A link to a more complete paper under preparation.
- Lecture 10 (2/20): We discussed three papers today: 1) Genetic Algorithm-based Clustering Technique by Maulik and Bandopadhyay, Pattern Recognition, Volume 33, ppp. 1455-65, 2000; 2) Dimensionality Reduction Using Genetic Algorithms by Raymer et al. in IEEE Transactions on Evolutionary Computation, Volume 4, No. 2, 2000; 3) Evolutionary Visual Art and Design, by Lewis, a chapter in The Art of Artificial Evolution, 2008, Springer.
- Lecture 11 (2/22): We read the paper Microgenetic Algorithms as Generalized Hill-Climbing Operators for GA Optimization by Kazarlis et al., IEEE Transactions on Evolutionary Computation, Volume 5, No. 3, 2001.
- Lecture 12 (2/27): We finished discussion of the Kazarlis et al. paper
- Lecture 13 (2/29): We read the paper Implementation of an Effective Hybrid GA for Large-Scale Traveling Salesman Problem by Nguyen et al., IEEE Transactions on Systems, Man and Cybernetics, Part B: Volume 37, No. 1, 2007.
- Lecture 14 (3/5): We read the paper An Effective Heuristic Algorithm for the Traveling Salesman Problem by Lin and Kernighan, Operations Research, Volume 21, No. 2, 1973.
- Lecture 15 (3/7): We read two papers. 1) A Parallel Genetic Algorithm for Performance-Driven VLSI Routing by Lienig, IEEE Transactions on Evolutionary Computation, Vol 1., No. 1, April 1997. 2) A Parallel Genetic Algorithm for Rule Discovery in Large Databases by Araujo et al., IEEE International Conference on Systems, Man, and Cybernetics, 1999.
- Lecture 16 (3/12): We discussed multi-objective optimization. Here is my presentation on Multi-objective Optimization. We looked at A fast and elitist multiobjective genetic algorithm: NSGA-II by Deb et al., IEEE Transactions on Evolutionary Computation, Volume 6, No. 2, pp. 182-197, 2002. We also read the paper Pareto Multi-objective Optimization by Ngatchou et al. in Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, 2005.
- Lecture 17 (3/14): Continuation of previous class.
- Lectures 18 and 19 (3/19 and 3/21): Student Mid-term presentations
- Lecture 20 (4/2): Differential Evolution. Here is my presentation on Differential Evolution. We discussed the paper Differential Evolution--A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces by Storn and Price, Journal of Global Optimization, 11: 341-359, 1997. We also discussed the paper Differential Evolution: A Survey of the State-of-the-Art by Das and Suganthan, IEEE Transactions on Evolutionary Computation, Volume 15, No. 1, 2011, pp. 4-31.
- Lecture 21 (4/4): Continuation from previous class
- Lecture 22 (4/9): Genetic Programming, Chapter 6 of Sivananda and Deepa. We also looked at the article A Genetic Programming Tutorial by Koza and Poli
- Lecture 23 (4/11):
We discussed the paper Bankruptcy theory development and classification via genetic programming, Lensberg et al., European Journal of Operations Research, 169, pp. 677-697, 2006.
- Lecture 24 (4/16): Particle Swarm Optimization: Chapter 11 of Sivanandan and Deepa. We also read the paper Particle Swarm Optimization by Kennedy and Eberhart from 1995.
- Lecture 25 (4/18): We read the following three papers: The particle swarm optimization algorithm: convergence analysis and parameter selection by Trelea, 2003; Comparison between genetic algorithms and particle swarm optimization by Eberhart and Shi, 1998; Data Clustering using Particle Swarm Optimization by van der Merwe and Engelbrecht, 2003.
- Lecture 26 (4/23): We read the paper A Discrete Binary Version of the Particle Swarm Algorithm by Kennedy and Eberhard, 1997.
- Lecture 27 (4/25): We discussed the paper Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem by Dorigo and Gambardell, IEEE Transactions on Evolutionary Computation.