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tion, the variable needs 3.3 bits in the gene. Thus a solution accurate to eight positions requires 8 3.3 = 26.4 or 27 bits in the gene. If there are 10 variables, then a chromosome has 270 bits. A long gene slows convergence of the GA. Lesson: Only represent the variables to the minimum accuracy necessary. In order to precisely estimate the gene size, one must rst determine the acceptable variable accuracy in the form of quantization error. Tolerances of parts, design speci cations, manufacturing limitations, and suf cient numerical accuracy contribute to determining the minimum quantization level. An advantage of the binary GA is that it forces the user to determine the accuracy of all variables in the optimization process. It is foolish to optimize a design only to nd that the tolerance of a part is too tight. This is like the government overspecifying tolerances on simple items and driving up the cost. So be careful to avoid the GA equivalent of the $500 hammer (or was it a $500 toilet seat ).

Consider the familiar Microsoft Excel 2000 (v. 9.0.2720) package. Place numbers 1, 2, and 3 in the rst column, and use STDDEV function to compute the standard deviation of these numbers. The correct answer is 1. Now add 99,999,999 to each number. Clearly, the standard deviation should not change by addition of any number. However, Excel gives the answer of zero! It appears that as long as the numbers are smaller than 100 million, Excel works all right. This is a serious problem with such popular software, which was pointed out to Microsoft years ago and still has not been xed. One explanation is the monopoly that Microsoft enjoys, and perhaps a more potent explanation is that the commercial users of the software do not care enough about numerical accuracy. 9.2.3 Binary Arithmetic

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Lamotrogine (Lamictal), 363, 378 Landau-Kleffner syndrome, 260 case study, 260 271 Language functioning, 1, 3, 5, 518, 519 and Angelman syndrome, 405 attention de cit hyperactivity disorder, 127, 129 autism spectrum disorders, 100 101, 104 childhood cancer, 214 diabetes type 1, 481 Down syndrome, 390 dyscalculia/math disability, 49 dyslexia/reading disability, 24 epilepsy, 242 243 fetal alcohol spectrum disorders, 367 fetal valproate syndrome, 379 fragile X, 395 Klinefelter syndrome, 422 low birth weight, 310 neuro bromatosis, 436 phenylketonuria (PKU), 497 Prader-Willi syndrome, 410 sickle cell disease, 277 speci c language impairment, 73 Sturge-Weber syndrome, 465 Tourette syndrome, 155, 157 traumatic brain injury (TBI), 189 tuberous sclerosis, 450 Turner syndrome, 419 Williams syndrome, 402 Lead exposure, 338 339, 342, 343 344 case study, 347 357 Learn at Play Program, 392, 393 Learning and memory, 6, 14, 519, 520 attention de cit hyperactivity disorder (ADHD), 128 autism spectrum disorders, 104 childhood cancers, 214 diabetes type 1, 481 Down syndrome, 390 dyscalculia/math disability, 49 dyslexia/reading disability, 16 epilepsy, 242, 243

Fitting an RMP to available time and other resources is central to the issue of short cuts (addressed in 15). For present purposes, we assume any necessary time is available. If the process timing is not predetermined by the need to make decisions by particular dates, a Gantt chart like that of Figure 4.2 can be used to plan the rst pass of the iterative process and an allowance made for further passes, as discussed in 4. The time required for the rst and later passes can re ect what has to be done and the resources available to do it. In this case the process when ows from the rst ve W s in the ideal project-planning manner. If when is constrained, the earlier W s will have to be constrained to make the process feasible, as discussed earlier.

Much work on pattern languages has focused on improving software development processes and organizations [Cope95] [Cock97] [Ris98] [Har96]. Some software development teams have applied these patterns to help improve the quality of their workplace [Gab96] and their productivity. This work is also beginning to influence the academic software process research community. We expect to see more integration between patterns and software development processes and organizations over the next few years. Patterns appear to be particularly useful for documenting 'lightweight' software development processes, such as open-source [OS99] and eXtreme Programming (XP) [Beck99] [KiLe00] processes. In addition, processes based on refactoring [Opd92] [FBBOR99] have been applying patterns successfully to help restructure existing software and enhance its modularity, maintainability, and reusability.

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