Abstract
An approach based on the behaviour of human genome is developed to efficiently provide a general framework for optimizing the use of space technology in surveying networks design. The developed approach attempts to apply the successful self-organizing principles based upon the biological evolution to artificial intelligence. It mimics the phenomena of natural selection observed in nature to achieve its goals by continuously adopting a population of candidate solutions and improving its performance over successive generations. The goal of adaptation is to find the best solution that optimizes the design of a surveying network based on the use of satellite observations. This network can be defined as a set of stations, co-ordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order for observing these sessions to give the best observation schedule at minimum cost. The obtained results prove the effectiveness of the developed technique in term of solution quality and computational efforts.
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