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## Selection of Evolution Parameters for Micro-GA

In principle, micro-genetic algorithms are similar to the standard genetic algorithm described in the previous section, in the sense of sharing the same evolution parameters and similar considerations. There is, however, an important distinction: since new genetic material is introduced into the population every time the algorithm is restarted, there is really no need for either jump or creep mutation. Also, elitism is required, at least every time the population is restarted, otherwise the algorithm would lose its exploitation capability. I have also found that the algorithm is much less sensitive to the choice of evolution parameters compared with the standard genetic algorithm. In particular, population sizes of 5 to 7 with crossover rates of 0.8 to 0.95 give very good results. The top panels of Figure  shows a comparison of convergence rates for populations of 3, 5 and 7 individuals. It seems clear that 5 individuals is the best. This result agrees with Carroll's who employed micro-GAs to optimize an engineering problem Carroll (1996). The bottom panels show a comparison of convergence rates for populations of 5 individuals and crossover rates of 0.7, 0.9 and 1.0. It seems that 0.9 is the best crossover rate, although further tests showed that 0.95 gave even better results and therefore that value was chosen for the remaining tests. Figure  shows the results in terms of trace match. Again, it is apparent that a population of 5 and a crossover rate of 0.9 are optimum. In particular, note how a uniform crossover of 1.0 (bottom right panel) is far too disruptive.

MG_compare1
Figure 9
Comparison of convergence rates for different options of the micro genetic algorithm. Top panel population rates of 3, 5 and 7 (from left to right). Bottom panels, with population size of 5 and crossover rate of 0.7, 0.9 and 1.0.

MG_compare2
Figure 10
Comparison of trace match for different options of the micro genetic algorithm. Continuous line is the reference trace and dotted line is the inverted trace. Top panel population rates of 3, 5 and 7 (from left to right). Bottom panels, with population size of 5 and crossover rate of 0.7, 0.9 and 1.0.

Next: Summary of Evolution Parameters Up: Micro-Genetic Algorithm Previous: Micro-Genetic Algorithm
Stanford Exploration Project
11/11/2002