This paper introduces a novel Genetic Algorithm (GA) for time efficient calculation of a solution to a resource management (RM) problem in the context of naval warfare. The novelty resides in the introduction of a new operator to correct the behavior observed in Steady State Genetic Algorithms (SSGA). The SSGA model differs from the traditional model in that it simulates the dynamics of a population reproducing in a semi-random way. It has been observed that genetic diversity is lost within a few generations when an SSGA is implemented using a small population . The main purpose of the new operator is the diversification of a population. Its performance is evaluated according to a measure of a population's diversity (entropy). The RM problem is also examined in detail; it is formulated as a non-linear optimization problem. The GA has been implemented using a proprietary data-driven multi-agent system, developed by Lockheed Martin Canada. The advantage of this novel GA over previous methods (TABU search) has been empirically confirmed by extensive simulations.
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