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The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.

Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks