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By Samet Hanan

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P, etc. is one of the exercises at the end of the chapter). Another useful measure is the convergence velocity [adapted from BA96q1511, Y: It is important that such performance measures are averaged over if sensible results are to be achieved. For complex multimodal functions, multiple runs are ~ ~ k toefind ~ the y same finai o p t i m and ~ one way of judging success is to plot a histogram of the number of times local optima of similar value were found. 3 show the effect of P, on fma (the maximum fitness in any generation) rather than&,, or&@ In general, it is probably better practice to plot the number of objective function evaluations on the abscissa rather than the generation.

The files are comma separated and can be loaded into most spreadsheets for analysis and data plotting. m. length) = 10 G, W a x . 3. A completed input screen for LGADOSEXE. The meaning of cm will described later, but it should be set to zero for now. - - To test LGADOS, and the stochastic nature of GAS, a simple example: can now be completed using LGADOS. 01 &=O c, = 0 Problem =fz After setting these, press ENTER. LGADOS will display a simple listing of the average and best fitness within a single generation, together with the best estimate of x.

Add together the fitness of the population members (one at a time) stopping immediately when the sum is greater than R,. The last individual added is the selected individual and a copy is passed to the next generation. Algorithm 2. Implementing fitness-proportional selection. The selection mechanism is applied twice (from Step 2) in order to select a pair of individuals to undergo, or not to undergo, crossover. Selection is continued until N (the population size, assumed to be even) individuals have been selected.

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