The ecosystem is an evolutionary result of natural laws. Food web (or food chain) embeds a set of computation rules of natural balance. Based on the concepts of food web, one of the laws that we may learn from the natural besides neural networks and genetic algorithms, we propose a theoretical computation model for mobile-agent evolution on the Internet. We define an agent niche overlap graph and agent evolution states. We also propose a set of algorithms, which is used in our multimedia search programs, to simulate agent evolution. Agents are cloned to live on a remote host station based on three different strategies: the brute force strategy, the semi-brute force strategy, and the selective strategy. Evaluations of different strategies are discussed. Guidelines of writing mobile-agent programs are proposed. The technique can be used in distributed information retrieval which allows the computation load to be added to servers, but significantly reduces the traffic of network communication. In the literature of software agents, it is hard to find other similar models. The results of this research only address a small portion of the ice field. We hope that this problem would be further studied in the societies of network communications, multimedia information retrieval, and intelligent systems on the Internet.
Relation:
International Journal of Information Science 137(1-4):53-73
