Download PDF by Roque Marín, Eva Onaindía, Alberto Bugarín, José Santos: Current Topics in Artificial Intelligence: 11th Conference

By Roque Marín, Eva Onaindía, Alberto Bugarín, José Santos

ISBN-10: 3540459146

ISBN-13: 9783540459149

This e-book constitutes the completely referred post-proceedings of the eleventh convention of the Spanish organization for man made Intelligence, CAEPIA 2005, held in Santiago de Compostela, Spain in November 2005.

The forty eight revised complete papers offered including an invited paper have been conscientiously chosen in the course of rounds of reviewing and development from an preliminary overall of 147 submissions. The papers span the full spectrum of man-made intelligence from foundational and theoretical concerns to complex functions in numerous fields.

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Thus, the human expert can be given detailed explanations as how the lower-level information has been combined in order to generate higherlevel information. It is also more suitable for agent-based implementation, where each agent can take charge of matching each finding, using the results from the previous agents. The heuristics that we have presented sacrifices the completeness of the matching algorithms. Nevertheless, it is highly probable that the solutions found will enable the global pattern, if it exists, to be found, since they act as a sampling of each occurrence of a finding.

Problem GHUGA DGA DGALS m n A. S A. S A. 36 100 250 500 100 250 500 100 250 500 24136 218304 491573 318764 475643 645250 197855 369894 587472 58045 140902 185606 70765 153475 179047 106542 184446 233452 99549 381840 675086 175630 495312 705148 217794 532870 716827 A Flipping Local Search Genetic Algorithm for the MKP 29 Table 2. Computational results for the 11 large instances. We compare DGALS with GLOVERA. S. and A. %gap of DGALS are based on 10 runs for each instance. Problem GLOVERA m n Best Sol.

A value α[j] = 0 or 1 in the j-bit means that variable xj = 0 or 1 in the represented solution. 2 Repair Operator and Improve Procedure The repair operator lies on the notion of utility ratios. Let K = (n, m, p, A, b) be an instance of MKP and let ω ∈ (0, 1]m be a vector of surrogate multipliers. p The utility ratio for variable xj is defined by uj = m jωi aij . Given an infeasible i=1 individual α ∈ {0, 1}n we apply the following repairing procedure. ,n) with u_P[j] <= u_P[j+1] for j=1 to n do if (alpha[P[j]]=1 and infeasible(alpha)) then alpha[P[j]]:=0 end Once we have transformed α into a feasible individual α , a second phase is applied in order to improve α .

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Current Topics in Artificial Intelligence: 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, Santiago de Compostela, by Roque Marín, Eva Onaindía, Alberto Bugarín, José Santos


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