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The payroll manager will almost certainly see court-ordered child support withholding orders at some point during his or her career There are tightly enforced federal laws that help to track down parents who are not making support payments and require employers to withhold various amounts from employees pay in order to meet mandated child support payments The maximum amount of an employee s disposable earnings that is subject to child support withholding is 60 percent of his or her pay, or 50 percent if the employee is already making payments to support other children or spouses Both percentages increase by 5 percent if an employee is 12 or more weeks in arrears in making support payments In order to calculate disposable earnings, subtract all legally mandated deductions from an employee s gross pay, such as federal and state income taxes, Social Security and Medicare taxes, and any locally mandated disability or unemployment taxes Voluntary deductions, such as pension and medical insurance deductions, are not used to calculate disposable earnings A good garnishment management technique is to keep a calendar itemizing the amount of each garnishment, the declining balance on each one, and the date on which the last deduction and related payment to a third party is due Also, there can be some dispute regarding the start date of the deductions, which are frequently tied to the date of receipt of a withholding order Consequently, mark and initial the date of receipt, as well as the postmark date, on the withholding order

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The rst applications of PSO was to train feedforward neural networks (FFNN) [224, 446] These rst studies in training FFNNs using PSO have shown that the PSO is an e cient alternative to NN training Since then, numerous studies have further explored the power of PSO as a training algorithm for a number of di erent NN architectures Studies have also shown for speci c applications that NNs trained using PSO provide more accurate results Section 1671 and Section 1671 respectively address supervised and unsupervised training NN architecture selection approaches are discussed in Section 1672

Enterprise scope and context Acknowledging that resources are insuf cient to replace the magnitude of legacy systems Necessity to keep pace with advancing technology Necessity to deliver signi cant improvement from constrained investment Make application least invasive while delivering immediate tangible bene t

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Supervised Learning The main objective in supervised NN training is to adjust a set of weights such that an objective (error) function is minimized Usually, the SSE error is used as the objective function (refer to equation (217))

Example: The Dim Bulb IQ Testing Company receives a court order to withhold child support payments from the pay of its employee, Ernest Evans, in the amount of $390 per weekly paycheck The payroll manager needs to determine how much can actually be withheld from Mr Evans s pay Mr Evans earns $850 per week and does not make support payments to another child or spouse His typical pay check remittance advice is as follows:

Gross pay Federal income tax State income tax Social Security tax Medicare tax Medical insurance 401k plan deduction Net pay

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In order to use PSO to train an NN, a suitable representation and tness function needs to be found Since the objective is to minimize the error function, the tness function is simply the given error function (eg the SSE given in equation (217)) Each particle represents a candidate solution to the optimization problem, and since the weights of a trained NN are a solution, a single particle represents one complete network Each component of a particle s position vector represents one NN weight or bias Using this representation, any of the PSO algorithms can be used to nd the best weights for an NN to minimize the error function Eberhart and Kennedy [224, 446] provided the rst results of applying the basic PSO to the training of FFNNs Mendes et al [575] evaluated the performance of di erent neighborhood topologies (refer to Section 162) on training FFNNs The topologies tested included the star, ring, pyramid, square and four clusters topologies Hirata et al [367], and Gudise and Venayagamoorthy [338] respectively evaluated the ability of lbest and gbest PSO to train FFNNs Al-Kazemi and Mohan applied the multiphase PSO (refer to Section 1654) to NN training Van den Bergh and Engelbrecht showed that the cooperative PSO (refer to Section 1654) and GCPSO (refer to Section 1651) perform very well as NN training algorithms for FFNNs [863, 864] Settles and Rylander also applied the cooperative PSO to NN training [776] He et al [359] used the basic PSO to train a fuzzy NN, after which accurate rules have been extracted from the trained network The real power of PSO as an NN training algorithm was illustrated by Engelbrecht and Ismail [247, 406, 407, 408] and Van den Bergh and Engelbrecht [867] in training NNs with product units The basic PSO and cooperative PSO have been shown to outperform optimizers such as gradient-descent, LeapFrog (refer to Section 324), scaled conjugate gradient (refer to Section 323) and genetic algorithms (refer to 9) Paquet and Engelbrecht [655, 656] have further illustrated the power of the linear PSO in training support vector machines Salerno [753] used the basic PSO to train Elman recurrent neural networks (RNN) The PSO was successful for simple problems, but failed to train an RNN for parsing natural language phrases Tsou and MacNish [854] also showed that the basic PSO fails to train certain RNNs, and developed a Newton-based PSO that successfully trained a RNN to learn regular language rules Juang [430] combined the PSO as an operator in a GA to evolve RNNs The PSO was used to enhance elitist individuals.

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