By Süntje Böttcher, Benjamin Doerr, Frank Neumann (auth.), Robert Schaefer, Carlos Cotta, Joanna Kołodziej, Günter Rudolph (eds.)
This e-book constitutes the refereed court cases of the eleventh overseas convention on Parallel challenge fixing from Nature - PPSN XI, held in Kraków, Poland, in September 2010. The 131 revised complete papers have been rigorously reviewed and chosen from 232 submissions. The convention covers a variety of issues, from evolutionary computation to swarm intelligence, from bio-inspired computing to genuine international functions. desktop studying and mathematical video games supported by means of evolutionary algorithms in addition to memetic, agent-oriented structures also are represented.
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Additional info for Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I
The ﬁrst signiﬁance value was always 1 (100%) in all of our experiments, conﬁrming that the ‘all algorithms were equivalent’ hypothesis is easily rejected. The second signiﬁance value varied widely, indicating a ‘distance’ in performance between the best and second-best algorithms. The size of the circle relates to this second signiﬁance measure. Broadly speaking, large indicates that the algorithm was much better than the second-best, and that the diﬀerence is likely to be signiﬁcant. A small circle means there was not a very distinct diﬀerence between the best and second best.
We refer to  for details of the proofs. Numerical simulation of convergence rates. To evaluate the improvements that can be brought about by mirrored samples and sequential selection, we now compare the different convergence rates. However, those convergence rates are expressed only implicitly as the expectation of some random variables. We therefore simulate the convergence rate with a Monte-Carlo technique. For each convergence rate expression, we have simulated 106 times the random variables inside the expectation and averaged to obtain an estimate of the convergence rate for different σ.
1. Left: If for a unimodal function with convex sub-level sets, a sampled solution is better than its parent (dark arrow into shaded region of better objective function values), the mirrored one (gray) is always worse. Right: Pseudocode for one iteration step of mirrored sampling and sequential selection, returning the new parent Xk+1 . N 0k+1 = N λk and before the first iteration, j is even. The pseudocode captures all combinations with/without mirrored sampling and/or sequential selection. The last line depicts comma-selection but can be replaced by plus selection.
Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I by Süntje Böttcher, Benjamin Doerr, Frank Neumann (auth.), Robert Schaefer, Carlos Cotta, Joanna Kołodziej, Günter Rudolph (eds.)