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Qin and Suganthan [698] propose that the probability of recombination be self-adapted as follows: (1324) pr (t) N ( pr (t), 01) where pr (0) = 05, and pr (t) is calculated as the average over successful values of pr (t) A pr (t) value can be considered as being successful if the tness of the best individual improved under that value of pr (t) It is not clear if one probability is used in [698] for the entire population, or if each individual has its own probability, pr,i (t) This approach to self-adaptation can, however, be applied for both scenarios For the self-adaptive Pareto DE, Abbass [2] adapts the probability of recombination dynamically as (1325) pr,i (t) = pr,i1 (t) + N (0, 1)[pr,i2 (t) pr,i3 (t)] where i1 = i2 = i3 = i U (1, , ns ), while sampling the scale factor from N (0, 1) Note that equation (1325) implies that each individual has its own, learned probability of recombination Omran et al [641] propose a self-adaptive DE strategy that makes use of the approach in equation (1325) to dynamically adapt the scale factor That is, for each individual, i (t) = i4 (t) + N (0, 05)[ i5 (t) i6 (t)] (1326)

1 6998 7175 7451 7628 7904 8081 8357 8534 8810 8987

where i4 = i5 = i6 = i U (1, , ns ) The mutation operator as given in equation (132) changes to (1327) ui (t) = xi1 (t) + i (t)[xi2 (t) + xi3 (t)] The crossover probability can be sampled from a Gaussian distribution as discussed above, or adapted according to equation (1325)

Lumer and Faieta [540] generalized the basic model of Deneubourg et al (refer to Section 1721) to cluster data vectors with real-valued elements for exploratory data analysis applications This section presents the original Lumer Faieta algorithm and discusses extensions and modi cations to improve the quality of solutions and to speed up the clustering process

2 6098 6275 6551 6728 7004 7181 7457 7634 7910 8087

.

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Lumer--Faieta Algorithm The rst problem to solve, in applying the basic model to real-valued vectors, is to de ne a distance, or dissimilarity, d(ya , yb ), between data vectors ya and yb , using any applicable norm For real-valued vectors, the Euclidean distance between the two vectors has been used most frequently to quantify dissimilarity The next problem is to determine how these dissimilarity measures should be used to group together similar data vectors, in such a way that intra-cluster distances are minimized; that is, the distances between data vectors within a cluster should be small to form a compact, condensed cluster inter-cluster distances are maximized; that is, the di erent clusters should be well separated As for the basic model, data vectors are placed randomly on a two-dimensional grid Ants move randomly around on the grid, while observing the surrounding area of n2 N sites, referred to as the nN -patch The surrounding area is simply a square neighborhood, NnN nN (i), of the nN nN sites surrounding the current position, i, of the ant Assume that an ant is on site i at time t, and nds data vector ya The local

3 5198 5475 5651 5928 6104 6381 6557 6834 7010 7287

density, (ya ), of data vector ya within the ant s neighborhood is then given as 1 d(ya , yb ) (ya ) = max 0, 2 1 (1745) nN

yb NnN nN (i)

Process engineering can be practiced at one of four levels 1 Unmanaged, Ad Hoc Processes, legacy and otherwise, are integrated into enterprise business operations locally on an ad hoc basis 2 Business Rule Standardization Processes are integrated into the enterprise business operations through business rules that tightly constrain the manner in which the process is built within application software Business rules are stored in various designer accessible repositories, registries, and spreadsheets Accuracy and synchronization are issues that have to be worked out on an interface-by-interface basis Manual governance (through meticulous checking of design rules) and manual con guration management prevail 3 Process Modeling Enterprise business processes are modeled with the process models stored on a generally accessible repository Application developers generally post and subscribe to the repository Process integration largely takes place at design-time and con guration management is exercised at the system level 4 Outcome-Driven Processes Enterprise business processes are modeled abstractly using enterprise level models as reference points The enterprise business process repository enables process integration through orchestration or choreography engines at run-time as well as through design-time methods Through enterprise modeling, all business processes are derived from comprehensive uni ed models, resulting in a model-driven architecture Con guration management is exercised at the enterprise level Enterprise Integration Enterprise integration has progressed from point-to-point connectivity, data hub connectivity, service-oriented architecture, enterprise service bus, and enterprise information interoperability manifesting in the service-oriented enterprise Figure 15 also illustrates the combining of net-centric enterprise services and semantic interoperability aggregating into SOE, the complete paradigm Your world is full of artifacts representing each of these paradigms: some point-topoint, some hub-and-spoke, and some implementations toward SOE Unfortunately, progress is a mishmash Point-to-point is much more costly than hub-and-spoke, and hub-and-spoke architectures are more costly than SOE The combination of con icting legacy causes the enterprise to lope toward a state of entropy The US government addresses enterprise-scale integration and performance optimization with different programs and with attempts at sharing knowledge, although the US government is far from having a cohesive strategy that integrates the thinking and powers of three branches of government New leadership must recognize the need for a common strategy and focus on America s public resource management that produces a common result a secure and prosperous nation.

4 4298 4575 4751 5028 5204 5481 5657 5934 6110 6387

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