We describe and verify convergence properties of our forced-based genetic algorithm (FGA) as a decentralized topology control mechanism distributed among software agents. FGA uses local information to guide autonomous mobile nodes over an unknown geographical terrain to obtain a uniform node distribution. Analyzing the convergence characteristics of FGA is difficult due to the stochastic nature of GA-based algorithms. Ergodic homogeneous Markov chains are used to describe the convergence characteristics of ...
We present bio-inspired computation techniques, such as genetic algorithms, for real-time self-deployment of mobile agents to carry out tasks similar to military applications. Under the harsh and bandwidth limited conditions imposed by military applications, self-spreading of autonomous mobile nodes becomes much more challenging. In our approach, each mobile agent exchanges its genetic information, which is composed of speed and direction encoded in its chromosome (genome), with the neighboring nodes located ...
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