Adaptive and Natural Computing Algorithms: 9th International by Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen,

By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)

This booklet constitutes the completely refereed post-proceedings of the ninth foreign convention on Adaptive and average Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.

The sixty three revised complete papers provided have been rigorously reviewed and chosen from a complete of 112 submissions. The papers are equipped in topical sections on impartial networks, evolutionary computation, studying, tender computing, bioinformatics in addition to applications.

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Additional info for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers

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1 ∗ ( evaluate solution S on set tab[i]); end return eV , eS end foreach D ∈ D2 do tab[1 . . 2] = split D into 2 parts; V = solution from the genetic algorithm run on set tab[1]; S = single best method found using set tab[1]; eV = evaluate solution V on set tab[2]; eS = evaluate solution S on set tab[2]; return eV , eS end end Algorithm 3. The test procedure Table 2. Test results. N - number of rows, NInc - number of incomplete rows, σeV , σeS - standard deviations. 0168 The results show that for all data sets a suitable method vector representing an imputation strategy has been found.

The present work is based on the following cost function formulation: J({Wl }) = 1 2N N i=1 N ({Wl })(xi ) − yi 2 L +β l=1 (i,j)∈Il 1 l 2 |Wi,j | 2Sl (2) for β ≥ 0. , first column) of WL as suggested by the test results in [3] (see also [5], Chapter 9). Sl is the number of elements in the index set Il . This averaging divisor was not present in the earlier works [3,4]. If we were to use only the first mean-squared-error term, and find a model near the global optimum of that cost function, it is quite likely that the model would overfit individual quirks of the training examples, and not be able to generalize into unforeseen vectors.

Decide a plan for successive betas to try; a basic choice is: 36 P. Nieminen and T. , 1e-6] Initialize tracking of best combination: beta_best = undefined. valE_best = Inf W_best = undefined. 2. Execute the main loop: For N_restarts times: W = random weights For each beta in beta_plan: W = network trained further from current values, using pre-train set, and current beta, loose accuracy. valE_this = sum-squared-error on pre-validation set If (valE_this beta_best valE_best W_best < = = = valE_best): beta valE_this W 3.

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