This paper demonstrates the applicability of evolutionary algorithms to the problem of motor parameter determination. Motor parameter determination problems can range from high accuracy requirement for motor controlled drives to low accuracy requirements for system studies. The later problem is addressed here, using both the genetic algorithm and genetic programming. Comparative results are presented. I Flow of control Required CPU time Oualitv of the result

In this paper, we present an agent architecture and a system structure that support fine-grained multiagent problem solving over the internet. In our model, each agent works on genetic programming tasks, resides at a workstation on the internet and is capable of coordinating its activities with a variable number of other agents using the same coordination protocol. The system of agents has a virtual topology that is structured as a loosely coupled network of domains, each of which contains some number of nodes in the overall system. Agents (nodes) cooperate via DGPP, a contract net-based high level communication and control protocol. A testbed based on the model has been implemented in Java. Several standard problems were tested. The results showed near linear speed up using six Pentium Pro/Windows NT workstations.

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