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The minimum and maximum tness of the current population is indicated by fmin (t) and fmax (t) respectively Directed variation is applied to each component of each individual as follows For component xij (t), the direction of mutation is determined by f (Ijs ), f (Ij,s 1 ) and f (Ij,s+1 ), where xij (t) Ijs If f (Ijs ) > f (Ij,s 1 ) and f (Ijs ) > f (Ij,s+1 ), no directed variation will be applied If f (Ij,s 1 ) > f (Ijs ) > f (Ij,s+1 ), then xij (t) moves toward f (Ijs ) subinterval Ij,s 1 with probability 1 f (Ij,s 1 ) The move is implemented by replacing xij (t) with a random number uniformly distributed between xij (t) and the middlepoint of the interval Ij,s 1 A similar approach is followed when f (Ij,s 1 ) < f (Ijs ) < f (Ij,s+1 ) If f (Ijs ) < f (Ij,s 1 ) and f (Ijs ) < f (Ij,s+1 ), then xij (t) moves toward any of its neighboring intervals with equal probability For the above, f (Ij0 ) = f (Ij,nI +1 ) = 0 Two approaches have been proposed to apply directed variation For the rst, directed variation is applied, after selection, to the members of the new population For the second strategy, each parent produces one o spring using directed variation Crossover is then applied as usual to create the remaining o spring The selection operator is applied to the parents, the o spring produced by directed variation, and the o spring produced by crossover

Gain the highest match accuracy Continuously improve accuracy over time Improve match quality by learning from data stewards Improve match rates based on both internal and external reference data

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Barlowe, Cindy Bosley, Sandra Caraway, Lisa Chantilly, Edward Davis, Humphrey Dunning, Mortimer Dunwiddy, Alexandra Greenstreet, James Guthrie, James Halverston, Eric Johnson, Noyes Montrose, Wesley Mortimer, Victoria Penway, Alice Quarterstaff, Molly Rosencrantz, Albert Saunders, Ricardo Von Gundle, Gerta Zeta, Xavier 5/14/01 9/27/99 4/26/99 10/30/95 8/20/01 9/4/01 8/13/01 4/16/01 5/14/01 10/27/97 2/14/94 6/21/99 11/10/99 8/9/99 5/6/98 5/22/95 5/29/01 2/14/94 9/20/99 8/14/02 4/1/03 4/1/03 6/1/03 8/20/02 12/1/02 2/1/03 4/16/03 5/16/03 4/1/03 2/1/03 11/16/02 4/1/03 5/16/03 10/11/02 10/16/02 6/1/02 1/14/02 9/1/03 31,927 72,574 125,000 42,567 31,927 31,927 42,567 27,249 27,249 62,286 85,180 52,936 85,000 42,567 72,574 31,927 47,997 63,742 41,500 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Software Developer I Senior Software Developer Chief Executive Officer Software Developer II Software Developer I Software Developer I Software Developer II Quality Review Analyst Quality Review Analyst Development Team Leader Vice President - Sales Quality Review Supervisor Chief Financial Officer Software Developer II Senior Software Developer Software Developer I Sales Representative, Sr Regional Sales Manager Secretary to CEO

Incremental ES search for an optimal solution by dividing the search process into nx phases one phase for each decision variable [597] Each phase consists of two steps The rst step applies a single variable evolution on the one decision variable, while the second step applies a multi-variable evolution after the rst phase For phase numbers less than nx , a context vector is needed to evaluate the tness of the partially evolved individual (similar to the CCGA and CPSO discussed in Sections 153 and 1654)

Surrogate methods have been developed speci cally for problems where the tness function is computationally expensive to evaluate The tness function is approximated by a set of basis functions, called surrogates Evaluation of the surrogate model is computationally less expensive than the original function The reader is referred to [860] for more detail on surrogate models for ES

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This section shows how ES can be used to solve constrained problems (Section 1261), multi-objective optimization problems (Section 1262), problems with dynamically changing optima (Section 1263), and to locate multiple optima (Section 1264)

This is one example of how technology is keeping pace with technical requirements for smart data strategy Data from Suppliers We discovered a company called Austin-Tetra with a product called Supplier Data Management that creates a consolidated view of enterprise supply base from fragmented, disparate and inconsistent data silos [7] We use this company s product as a basis for comparing and contrasting smart data strategy The solution leverages best-in-class content, extensive knowledge base, and proprietary technology to support better business decisions, reducing costs and risk [7]

While a number of ES variations have been developed to cope with constraints, this section discusses only some of these approaches Tahk and Sun [830] converted the constrained problem to an unconstrained problem using the augmented Lagrangian approach given in Section A62 A coevolutionary approach is used to nd the saddle point, (x , , ), of the Lagrangian given in equag h tion (A27) Two populations are used, each with di erent objectives, both evolved in parallel Assuming a minimization problem, the one population minimizes the tness function, (1246) f (x) = max L(x, g , h )

$32,240 $51,039 $200,000 $46,200 $31,200 $33,280 $42,400 $53,000 $37,752 $80,250 $76,000 $57,500 $100,000 $46,824 $80,000 $50,000 $49,920 $60,000 $47,700

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