1. Fundamentals of Optimization Problems 
Ernst W. Mayr
Slides 
 G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. MarchettiSpaccamela, M. Protasi:
Complexity and Approximation  Combinatorial Optimization Problems and Their Approximability Problems, pp. 2238 pdf, 87152 pdf
SpringerVerlag: BerlinHeidelbergNew York, 1999
 J. Hromkovič:
Algorithmics for Hard Problems  Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics, pp. 213238
SpringerVerlag: BerlinHeidelbergNew York, 2001
pdf
 R. Wanka:
Approximationsalgorithmen  Eine Einführung, pp. 8185
B.G. Teubner Verlag: Wiesbaden, 2006
pdf

2. The Knapsack Problem 
Florian Pawlik
Slides,
Paper

 J. Hromkovič:
Algorithmics for Hard Problems  Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics, pp. 238248
SpringerVerlag: BerlinHeidelbergNew York, 2001
pdf

3. Dynamic Programming for Counting the Number of Knapsack Solutions 
Julius Kobold
Slides, Paper 
 M. Dyer.
Approximate counting by dynamic programming.
in: Proc. 35th ACM Symp. on Theory of Computing (STOC), pp. 693699, 2003.
10.1145/780542.780643
pdf
 P. Gopalan, A. Klivans, R. Meka, D. Stefankovic, S. Vempala,
E. Vigoda.
An FPTAS for #Knapsack and Related Counting Problems.
in: Proc. 52nd IEEE Symp. on Foundations of Computer Science (FOCS),
pp. 817826, 2011
doi:10.1109/FOCS.2011.32
pdf

4. Blind Search 
Sophia Koch
Slides, Paper 
 M. Dietzfelbinger, J. E. Rowe, I. Wegener, Ph. Woelfel:
Tight Bounds for Blind Search on the Integers and the Reals.
in: Combinatorics, Probability and Computing 19 (2010) 711728.
doi:10.1017/S0963548309990599
pdf

5. Particle Swarm Optimization (PSO) and Parameter Selection 
Christoph Strößner
Slides,
Paper
 
M. Jiang, Y. P. Luo, S. Y. Yang:
Particle swarm optimization  stochastic trajectory
analysis and parameter selection.
In Felix T. S. Chan and Manoj Kumar Tiwari, editors,
Swarm Intelligence  Focus on Ant and Particle Swarm Optimization, chapter 17, pages 179198. ITECH Education and Publishing, Vienna, 2007.
doi:10.5772/5104
pdf
For background information, also refer to:
Sabine Helwig.
Particle Swarms for Constrained
Optimization.
Ph.D. Thesis, University of ErlangenNuremberg, 2010.
(full text is available here)

6. Particle Swarm Optimization for Discrete Problems (PSO) 
Yushan Liu
Slides, Paper 
 M. Clerc:
Discrete Particle Swarm Optimization, illustrated by
the Traveling Salesman Problem
2000.
Paper and source files
 M. Hoffmann, M. Mühlenthaler, S. Helwig, R. Wanka:
Discrete Particle Swarm Optimization for TSP: Theoretical Results and Experimental Evaluations.
in: Proc. International Conference on Adaptive and Intelligent Systems (ICAIS), pp. 416427, 2011.
doi:10.1007/9783642238574_40
pdf

7. Evolutionary Algorithms for Sorting and Shortest Path Computation (EA) 
Dominik Schmid
Slides, Paper 
 J. Scharnow, K. Tinnefeld, I. Wegener:
The Analysis of Evolutionary Algorithms on Sorting and Shortest Paths Problems.
Journal of Mathematical Modelling and Algorithms
3 (2004) 349366.
doi:10.1023/B:JMMA.0000049379.14872.f5
pdf

8. Evolutionary Algorithms for Minimum Spanning Trees (EA) 
Martin Bullinger
Slides,
Paper 
 F. Neumann, I. Wegener:
Randomized local search, evolutionary algorithms, and
the minimum spanning tree problem.
Theoretical Computer Science
378 (2007) 3240.
doi:10.1016/j.tcs.2006.11.002
pdf

9. A Slime Mold Computes Shortest Paths 
Lukas Eisert
Slides, Paper 
 L. Becchetti, V. Bonifaci, M. Dirnberger, A. Karrenbauer, K. Mehlhorn.
Physarum Can Compute Shortest Paths: Convergence Proofs and Complexity Bounds.
in: Proc. 40th Int'l Colloquium on Automata, Languages, and Programming (ICALP), Part II, pp. 472483, 2013.
doi:10.1007/9783642392122_42
pdf

10. The Satisfiability Problem: Random Walk and Derandomization 
Timo Eckstein
Slides, Paper 
 U. Schöning.
A probabilistic algorithm for kSAT based on limited
local search and restart.
Algorithmica 32 (2002) 615623.
Paper beim Autor
pdf
 R. A. Moser, D. Scheder.
A Full Derandomization of Schöning's kSAT Algorithm.
in: Proc. 43rd ACM Symp. on Theory of Computing (STOC), pp. 245251, 2011.
doi:10.1145/1993636.1993670
pdf

11. Constructive Proof of the Lovasz Local Lemma 
Katharina Angermeier
Slides, Paper 
 H. Gebauer, R. A. Moser, D. Scheder, E. Welzl.
The Lovasz Local Lemma and Satisfiability.
Efficient Algorithms  Essays Dedicated to Kurt Mehlhorn on the Occasion of His 60th Birthday, pp. 3054, 2009.
doi:10.1007/9783642034565_3
pdf

12. Counting the Number of Satisfying Assignments 
Stefan Ploner
Slides, Paper 
 M. Thurley.
An approximation algorithm for #kSAT
in: Proc. 29th Int. Symp. on Theoretical Aspects of Computer Science (STACS), pp. 7887.
doi:10.4230/LIPIcs.STACS.2012.78
pdf
 M. Schmitt, R. Wanka.
Exploiting Independent Subformulas: A Faster Approximation Scheme for #kSAT
Information Processing Letters (2013), pp. 337344.
doi:10.1016/j.ipl.2013.02.013
pdf

13. Randomized Optimization Algorithms 
Manuel Schmitt
Slides 
 D.S. Hochbaum:
Approximation Algorithms for NPhard Problems, pp. 447459
PWS Publishing Company: Boston, 1997
pdf
 G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. MarchettiSpaccamela, M. Protasi:
Complexity and Approximation  Combinatorial Optimization Problems and Their Approximability Problems, pp. 153174
SpringerVerlag: BerlinHeidelbergNew York, 1999
pdf

14. Approaches based on the PrimalDual Paradigm 
Arya Moorthy

 D.S. Hochbaum:
Approximation Algorithms for NPhard Problems, pp. 144165
PWS Publishing Company: Boston, 1997
pdf
