By H. Katsumata
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Note the differences between Figs. 3 a) and 2 c), and between Figs. 3 b) and 2 d). 5 Conclusions The results documented here support the claim that heterogeneity is a domainspecific property. Using an evolutionary algorithm applied to two task domains, selection pressure consistently evolved heterogeneous agent groups for the one task, and homogeneous groups for the other. Bloat  and random diffusion have been cited as two possible alternative explanations for the repeated appearance of multiple behavioural classes in the TMP, but these hypotheses are refuted by the repeated convergence to a single behavioural class in the food foraging problem (see Figs.
44 with a maximum value of 186), but the process is much slower than that by recombination alone. 27 generations on average (73 generations in the worst run). Thus, the MBGPsystem with recombination and mutation shows both convergence velocity and reliability, when applied to the 8-parity problem. 6 MBEA and representation In the preceding section we have described how a MBGP-system with OBDDs can be designed for finding Boolean functions. We feel appropriate to give some final comments on this choice of representation.
The thick lines in Fig. 2 report data generated by 30 runs of the Legion system applied to the food foraging problem. Figs. 2 a) and b) plot the heterogeneity (as given in Eqn. 2) and the number of behavioural classes of the most fit ant group at the end of each generation in a single run of the Legion system. Figs. C. Bongard Fig. 2. Changes in heterogeneity for a set of runs of the travelling mailman and the food foraging problems: a) and b) show changes in the heterogeneity and the number of behavioural classes, respectively, of the fittest agent group at each generation during a typical run; c) shows changes in the average heterogeneity of the Legion population; d) shows changes in the average number of behavioural classes for the population.