Strategy parameter

Table 9.2 Mixed-integer evolution strategy user defined strategy parameters.

The (//., k, X)-selection chooses the best /i individuals from the union of /i parents and X offspring, except those parent individuals which exceed the maximum age of k generations. The consideration of a maximum age enables the ES to escape from local optima. Table 9.2 summarizes the user defined strategy parameters of the ES. 

In the first study, optimal component and control strategy parameters were derived to provide the best fuel economy on 4 individual drive cycles. The drive cycles included... 

Search points are -dimensional vectors in the real Euclidean space. Each individual may include additional strategy parameters, as follows n variances Cii = af, n n — l)/2 covariances c,y, of the normal distribution. Often, only variances are used, and sometimes there is a single common variance for all n variables. These strategy parameters determine the mutability of variables. 

Recombination is done not only for variables, but also for strategy parameters. It may be realized in the usual manner, by randomly selecting two parents to recombine, or it may be realized in a global maimer, in which the parents are separately selected for each variable or parameter. A few recombination rules are the following (a) without recombination (select the first parent) (b) discrete recombination (select one of the parents randomly) (c) intermediate recombination (select a random point on the segment between the two parents). Empirically, the best results are obtained with discrete recombination of variables and intermediate recombination of parameters. 

Initially, the variables are vectors from a bounded subspace of R". Afterwards, the search domain is extended to the whole space. The Meta-evolutionary programming approach also incorporates strategy parameters (standard deviations) in this representation. 

Figure 4 shows the ES as developed by Rechenberg and Schwefel. Historically ESs were designed for parameter optimization problems. The encoding used in an individual is therefore a list of real numbers these are called the object variables of the problem. Additionally, each individual contains a number of strategy parameters, these being the variances and covariances of the object variables (the covariances are... 

At the start of the development, it had been intended use an expert system shell to implement this tool, however, after careful consideration, it was concluded that this was not the optimum strategy. An examination procedure can be considered as consisting of two parts fixed documentary information and variable parameters. For the fixed documentary information, a hypertext-like browser can be incorporated to provide point-and-click navigation through the standard. For the variable parameters, such as probe scanning paths, the decisions involved are too complex to be easily specified in a set of rules. Therefore a software module was developed to perfonn calculations on 3D geometric models, created fi om templates scaled by the user. 

In general, Laiigeviii dynamics sim illation s run much the same as nioleciilar dynamics simulations. There are differences due Lo the presence of additional forces. Most of the earlier discussions (see pages 69-yO an d p. 3 10-327 of this man ual) on simulation parameters and strategies for molecular dyn amics also apply to Lan gevin dynamics exceptions and additional con sideraiion s are noted below. 

Our strategy in proceeding, therefore, is to write separate expressions for the forces cited in items (1) and (2), and then set them equal to each other as required by item (3). Since we have discussed osmotic effects in Chap. 8 and elastic forces in Chap. 3, we shall invoke certain concepts and relationships from these chapters in this discussion. In this derivation we continue to omit numerical coefficients and some of the less pertinent parameters (although we retain Vj for the sake of Problem 5 at the end of the chapter), and focus attention on the relationship between a, M, and the interaction parameter x-... 

The above treatment is predicated on the assumption that the kinetic energies of the photoelectrons from atoms A and B are close in energy. In the event that this assumption does not hold, then all of the instmmental parameters do not cancel for these equations, and the situation is more complex. An alternative strategy in this case is to compare the spectmm of the unknown matedal with a spectmm acquired under identical conditions of a pure standard reference matedal containing A and B that is close in suspected composition to the unknown. In this case. 

Subject-Based Retrieval Parameters. There are numerous means by which the subject content of a patent can be expressed, and which a searcher can use in developing a search strategy. Different databases offer differing subsets of these means. Effective strategies should in general not be limited to a single type of retrieval parameter rather, they should be built from different parameters and modified as needed to provide the strategy best fitted to the subject at hand. 

Patent classification codes are another subject-search parameter available in most patent databases. IPC codes are usually present and U.S. codes exist in a number of files in the case of Japan Patent Information Organization (JAPIO), Japanese codes too are available. It is possible to mimic a hand search by limiting operations to references falling within one class or group of classes. Although such strategies can in some instances be justified, it is usually wiser to treat class codes as just one of the various subject parameters that make up a search strategy. 

Adaptive Control. An adaptive control strategy is one in which the controller characteristics, ie, the algorithm or the control parameters within it, are automatically adjusted for changes in the dynamic characteristics of the process itself (34). The incentives for an adaptive control strategy generally arise from two factors common in many process plants (/) the process and portions thereof are really nonlinear and (2) the process state, environment, and equipment s performance all vary over time. Because of these factors, the process gain and process time constants vary with process conditions, eg, flow rates and temperatures, and over time. Often such variations do not cause an unacceptable problem. In some instances, however, these variations do cause deterioration in control performance, and the controllers need to be retuned for the different conditions. 

The Smith predictor is a model-based control strategy that involves a more complicated block diagram than that for a conventional feedback controller, although a PID controller is still central to the control strategy (see Fig. 8-37). The key concept is based on better coordination of the timing of manipulated variable action. The loop configuration takes into account the facd that the current controlled variable measurement is not a result of the current manipulated variable action, but the value taken 0 time units earlier. Time-delay compensation can yield excellent performance however, if the process model parameters change (especially the time delay), the Smith predictor performance will deteriorate and is not recommended unless other precautions are taken. 

The MPC control problem illustrated in Eqs. (8-66) to (8-71) contains a variety of design parameters model horizon N, prediction horizon p, control horizon m, weighting factors Wj, move suppression factor 6, the constraint limits Bj, Q, and Dj, and the sampling period At. Some of these parameters can be used to tune the MPC strategy, notably the move suppression faclor 6, but details remain largely proprietary. One commercial controller, Honeywell s RMPCT (Robust Multivariable Predictive Control Technology), provides default tuning parameters based on the dynamic process model and the model uncertainty. 

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