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clusters, recombination proceeds as follows: One of the clusters is randomly selected, and roulette wheel selection is used to select one of the individuals from this cluster as one of the parents The second parent is selected from any cluster, but such that individuals from remote clusters are selected with a low probability Over time, clusters tend to form over local minima Shir and B ck [785] proposed a dynamic niching algorithm for ES that identi es tnessa peaks using a peak identi cation algorithm Each tness-peak will correspond to one of the niches For each generation, the dynamic niche ES applies the following steps: The mutation operator is applied to each individual After determination of the tness of each individual, the dynamic peak identi cation algorithm is applied to identify nK niches Individuals are assigned to their closest niche, after which recombination is applied within niche boundaries If required, niches are populated with randomly generated individuals If nK niches have to be formed, they are created such that their peaks are a distance r of at least n nK from one another, where x r= 1 2

When supporting the Lockheed Martin Joint Strike Fighter SPOE program, Lockheed and the government customer had certain objectives and requirements Technical Objectives

(xmax,j xmin,j )2

$34,000

(1250)

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Peaks are formed from the current individuals of the population, by sequentially conr sidering each as a tness peak If an individual is n nK from already de ned tness x peaks, then that individual is considered as the next peak This process continues until nK peaks have been found or until all individuals have been considered Recombination considers a uniform distribution of resources Each peak is allowed = n parents, and produces = n o spring in every generation For each K K o spring are created as follows: One of the parents is selected from the peak, individuals of that niche using tournament selection The other parent, which should di er from the rst parent, is selected as the best individual of that niche If a niche contains only one individual, the second parent will be the best individual from another niche Intermediate recombination is used for the strategy parameters, and discrete recombination for the genotypes The selection operator selects individuals for the next generation by selecting n of the best o spring, along with the best n parents from that niche If there are not enough parents to make up a total of individuals, the remainder is lled up by generating random individuals New niches are generated at each generation Repeated application of the above process results in niches, where each niche represents a di erent solution Shir and B ck [785] expanded the approach above to a (1, )-ES with corrrelated a mutations

$31,005

Virtual exchanges instead of xed integration points Not platform dependent Not site dependent Can accommodate multiple co-op/security strategies Incremental integration, not big bang style solution New integrations do not disturb legacy integrations Synchronization of budgets and schedules not required Enterprise context instead of data stovepiping Local information models are maintained as interoperability is achieved Reduction of glue code over point-to-point integration Mediation requires less custom code than point-to-point integration SPOE reduces interface adaptor requirements Service component architecture Service-oriented solution with coarse (not chatty) service exchanges Best practices and patterns for SOA design SOA component guidelines universal SOA deployment Pluggable services that can be leveraged alongside many COTS middleware providers Source or custom deployments as needed by SPOE consumers

Table 121 Applications of Evolution Strategies Application Class Parameter optimization Controller design Induction motor design Neural network training Transformer design Computer security Power systems References [256, 443, 548, 679] [59, 456, 646] [464] [59, 550] [952] [52] [204]

$64,189 $56,000 $4,880 $41,502 $77,205 $43,521 $53,500

The rst application of ES was in experimental optimization as applied to hydrodynamical problems [708] Since then most new ES implementations were tested on functional optimization problems ES have, however, been applied to a number of real-world problems, as summarized in Table 121 (note that this is not an exhaustive list of applications)

Data About Competitors or Enemies On the verge of a new era in transparency, much more information about your competitors will be in the sunshine, so to speak If your enterprise is commercial and you address the needs of a broad consumer public, the chances are you will discover more about your competitors than if you are tightly focused on a niche Even then, there are advantages about operating in a smaller space, although customers and competitors tend to be more tight-lipped In the government space, information about competitors is widely available from both direct and indirect sources The question is, what is being done about it Is it prepared such that it is actionable Often, it is treated more tactically than strategically Decisions are often made based on old beliefs more so than facts There are layers and layers of information about enemies and competitors in which to delve for better decision support The most ef cient and effective way to leverage data resources that answer dif cult questions is through smart data Data About Economic Environment We are now overwhelmed with information about the economic problems of the world Uncertainty about the effectiveness of government intervention and mitigation strategies makes risks apparent To break the

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