An information perspective on evolutionary computation
Pages 3317 - 3334
References
[1]
D.H. Wolpert and W.G. Macready. No free lunch theorems for optimization. IEEE Trans Evolutionary Computation, 4:67--82, 1997.
[2]
C. Schumacher and M. Vose and D. Whitley. The No Free Lunch and Problem Description Length. GECCO 2001.
[3]
W.G. Macready and D.H. Wolpert. What makes an optimization problem hard?. Complex, 1:5:40--46, 1996.
[4]
P. Grünwald and P. Vitanyi. Algorithmic Complexity. Handbook on the Philosophy of Information. To appear.
[5]
T. English. Optimization is Easy and Learning is Hard In the Typical Function. Proc. 2000 Congress on Evolutionary Computation (CEC 2000), pages 924--931, 2000.
[6]
H. Buhrman, M. Li, J. Tromp, P. Vitanyi. Kolmogorov Random Graphs And The Incompressibility Method. SIAM Journal on Computing, 29:590--599.
[7]
Y. Borenstein, R. Poli. Kolmogorov complexity, Optimization and Hardness, IEEE CEC 2006
[8]
Y. Borenstein, R. Poli. Information Perspective of Optimization. PPSN 2006: 102--111.
[9]
Y. Borenstein. What Makes an Optimization Problem Hard? An Information-Theoretic Perspective. PhD Thesis, Chapter 3.
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Published: 08 July 2009
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