Download Algorithmic Learning Theory: 8th International Workshop, ALT by Manuel Blum (auth.), Ming Li, Akira Maruoka (eds.) PDF

By Manuel Blum (auth.), Ming Li, Akira Maruoka (eds.)

This publication constitutes the refereed complaints of the eighth overseas Workshop on Algorithmic studying thought, ALT'97, held in Sendai, Japan, in October 1997.
The quantity provides 26 revised complete papers chosen from forty two submissions. additionally incorporated are 3 invited papers by means of prime researchers. one of the themes addressed are PAC studying, studying algorithms, inductive studying, inductive inference, studying from examples, game-theoretical elements, determination techniques, language studying, neural algorithms, and diverse different elements of computational studying theory.

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Additional info for Algorithmic Learning Theory: 8th International Workshop, ALT '97 Sendai, Japan, October 6–8, 1997 Proceedings

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4, we show experimental results on the 2-500 and 4-500 test problems by our MOMA with the weighted sum-based local search. The height of each bar in Fig. 4 shows the average value of the hypervolume measure for the corresponding combination of the local search application probability PLS and the termination condition of local search NLS . It should be noted that our MOMA is exactly the same as NSGA-II when PLS = 0 and/or NLS = 0. That is, the left-most column and the bottom row in each plot in Fig.

Y dominates x: y is better than x): ∀ i, fi (x) ≤ fi (y) and ∃ j, f j (x) < f j (y). (3) When this relation does not hold for a pair of solutions, they are non-dominated with each other. That is, x and y are non-dominated with each other when we can not say which is better between them using the Pareto dominance relation in (3). If there is no solution y in a solution set that dominates x, x is a non-dominated solution in that solution set. When all solutions are non-dominated, the solution set is called a nondominated solution set.

The tournament size was 20 in Fig. 6 while it was 2 in this figure. initial solutions for local search. In the above-mentioned computational experiments in Fig. 6, we used the weighted sum-based tournament selection with tournament size 20 in the selection of initial solutions for local search. We also performed computational experiments using binary tournament selection. Experimental results are summarized in Fig. 11. From the comparison between Fig. 6 and Fig. 11, we can see that the performance of our MOMA was deteriorated by decreasing the tournament size in the selection of initial solutions for local search.

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