http://ieeexplore.ieee.org TOC Alert for Publication# 4235 2009 November 19 13 5 C1 C1 94 13 5 C2 C2 37 p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.]]> 13 5 945 958 770 13 5 959 972 619 13 5 973 990 432 k-nn classification. The performance of the proposed fuzzy categorical data-clustering techniques has been compared with that of some other widely used algorithms, both quantitatively and qualitatively. For this purpose, various synthetic and real-life categorical datasets have been considered. Also, a statistical significance test has been conducted to establish the significant superiority of the proposed multiobjective approach.]]> 13 5 991 1005 2126 13 5 1006 1029 886 k-means cluster analysis to eliminate the cardinality constraint and thereby simplify the mathematical model and the evolutionary optimization process. An evolution strategy which is a variant of the conventional ( mu+ lambda) evolution strategy but employs real coded genes with genetic inheritance operators such as arithmetic variable point cross over and real number uniform mutation to initiate a fast converging reproduction process has been evolved to solve the simplified model. The strategy also employs refined weight standardization algorithms to tackle the bounding and class constraints. Experimental results have been demonstrated on the Bombay Stock Exchange, India (BSE200 index, Period: July 2001-July 2006) and on the Tokyo Stock Exchange, Japan (Nikkei225 index, Period: March 2002-March 2007) datasets and compared with those obtained by the Markowitz mean-variance, random matrix theory filtered, and quadratic programming-based solution models for the appropriate cases.]]> 13 5 1030 1053 1341 13 5 1054 1074 1843 n log n) can be proven. In this paper, we derive a lower bound of Omega(n log n) for the complexity of computing the hypervolume indicator in any number of dimensions d > 1 by reducing the so-called uniformgap problem to it. For the 3-D case, we also present a matching upper bound of O(n log n) comparisons that is obtained by extending an algorithm for finding the maxima of a point set.]]> 13 5 1075 1082 352 13 5 1083 1092 247 13 5 1093 1119 1792 13 5 1120 1132 1174 13 5 1133 1150 1066 13 5 1151 1166 321 13 5 1167 1189 13916 13 5 1190 1192 150 13 5 1193 1193 38 13 5 1194 1194 197 13 5 1195 1196 1065 13 5 C3 C3 36 13 5 C4 C4 31