Evolutionary Multi-Agent Systems by Aleksander Byrski, Marek Kisiel-Dorohinicki

By Aleksander Byrski, Marek Kisiel-Dorohinicki

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4), one can expect here similar advantages as offered by them. First of all the diversity of the population should increase, followed by the so-called convergence reliability [12], that becomes very important in the practical applications of EMAS as a technique of solving the problems with unknown characteristics. The mechanisms supporting of the creation of niches make this approach particularly useful for solving multi-modal and multi-criteria problems (cf. [244]). Evolving of Phenotype In the case of applying the genetic evolution model (similar to genetic algorithms), one can see the subsequent characteristic feature of EMAS.

As a consequence, the agent can gain or loose the possibility of realizing of certain actions, in particular the action of reproduction, or even, when the energy level becomes lower than the death threshold, it will be removed from the system. Assuming that each change of the energetic level of the agent is accompanied by a reversed change of the energy of a certain agent or the environment, one can achieve the effect of maintaining the constant value of the total energy in the whole system, forming so called energy preserving rule: N ei [t] = const i=1 where N stands for the number of the agents in the population at the time t.

Baldwinian memetics are implemented) and may decide which local search methods are used, which individuals are subject to this process etc. • Meta-scheduler retains and provides historical information to the local search method, making possible implementation of tabu-search like techniques. The memetic algorithm may consist of all schedulers, but whether it becomes better, or simply more complex, can only may be discovered in the course of experiments. Memetic algorithms may be perceived as LTH hybrids, according to Talbi’s classification (see Sect.

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