User manual PALISADE EVOLVER 5.5

[. . . ] Guide to Using Evolver The Genetic Algorithm Solver for Microsoft Excel Version 5. 5 March, 2009 Palisade Corporation 798 Cascadilla St. Ithaca, NY USA 14850 (607) 277-8000 (607) 277-8001 (fax) http://www. palisade. com (website) sales@palisade. com (e-mail) Copyright Notice Copyright © 2009, Palisade Corporation. Trademark Acknowledgments Microsoft, Excel and Windows are registered trademarks of Microsoft, Inc. IBM is a registered trademark of International Business Machines, Inc. Palisade, Evolver, TopRank, BestFit and RISKview are registered trademarks of Palisade Corporation. [. . . ] A Crossover rate of 1 means that no crossover will occur, so only clones of the parents will be evaluated. Once Evolver has started solving a problem, you can change the crossover rate by using the Evolver Watcher (see the Evolver Watcher section in this chapter). Chapter 5: Evolver Reference Guide 103 · Mutation Rate. The mutation rate can be set to between 0. 0 and 1. 0, and reflects the likelihood that future scenarios will contain some random values. A higher mutation rate simply means that more mutations or random "gene" values will be introduced into the population. Because mutation occurs after crossover, setting the mutation rate to 1 (100% random values) will effectively prevent the crossover from having any effect, and Evolver will generate totally random scenarios. If all the data of the optimal solution was somewhere in the population, then the crossover operator alone would be enough to eventually piece together the solution. Mutation has proven to be a powerful force in the biological world for many of the same reasons that it is needed in a genetic algorithm: it is vital to maintaining a diverse population of individual organisms, thereby preventing the population from becoming too rigid, and unable to adapt to a dynamic environment. As in a genetic algorithm, it is often the genetic mutations in animals which eventually lead to the development of critical new functions. For most purposes, the default mutation setting does not need adjustment, but can, however, be changed by experienced users to fine-tune Evolver's performance on complex problems. The user may wish to boost the mutation rate if Evolver's population is fairly homogenous, and no new solutions have been found in the last several hundred trials. Once Evolver has started solving a problem, you can change the mutation rate dynamically by using the Evolver Watcher (see the Evolver Watcher section later in this chapter). By selecting Auto from the drop down list in the Mutation rate field, auto-mutation rate adjustment is selected. Auto-mutation rate adjustment allows Evolver to increase the mutation rate automatically when an organism "ages" significantly; that is, it has remained in place over an extended number of trials. For many models, especially where the optimal mutation rate is not known, selecting Auto can give better results faster. Number of Time Blocks and Constraint Cells Preceding Tasks For more information on these options, see the Schedule Solving method in the Solving Methods section of this chapter. For more information on these options, see the Project Solving method in the Solving Methods section of this chapter. 104 Model Definition Command Operators Evolver includes selectable genetic operators when used with the Recipe solving method. Clicking the Operators tab in the Adjustable Cell Group Settings Dialog allows you to select a specific genetic operator (such as heuristic crossover or boundary mutation) to be used when generating possible values for a set of adjustable cells. In addition, you can have Evolver automatically test all available operators and identify the best performing one for your problem. Genetic algorithms use genetic operators to create new members of the population from current members. Two of the types of genetic operators Evolver employs are mutation and crossover. The mutation operator determines if random changes in "genes" (variables) will occur and how they occur. The crossover operator determines how pairs of members in a population swap genes to produce "offspring" that may be better answers than either of their "parents". Evolver includes the following specialized genetic operators: Arithmetic Crossover ­ Creates new offspring by arithmetically combining the two parents (as opposed to swapping genes). Heuristic Crossover ­ Uses values produced by the parents to determine how the offspring is produced. [. . . ] Such random numbers are the basis for other routines that convert them into samples drawn from specific distribution types. See random sample, seed A random sample is a value that has been chosen from a probability distribution describing a random variable. Such a sample is drawn randomly according to a sampling "algorithm". The frequency distribution constructed from a large number of random samples drawn by such an algorithm will closely approximate the probability distribution for which the algorithm was designed. In Evolver: Population Probability Probability Distribution Random Number Generator Random Sample Ranges The user sets the range, or the highest and lowest value that Evolver is allowed to try when adjusting a certain variable. [. . . ]