Comparing the Robustness of Market-Based Task Assignment to Genetic Algorithm

In previous works we developed a market-based solution to the problem of assigning mobile agents to tasks. We showed that this solution is near-optimal, quick, and can naturally incorporate fuzzy logic to deal with uncertainty in task locations to reduce sensitivity. So far we have only hypothesized that the market-based can naturally handle changes in the scenario and do it more eciently than other near-optimal algorithms. In this work we show that the market-based solution is very robust, and handles changes in the scenario better than a commonly used genetic algorithm. This is true both for cases when the changes occur at relatively low rates and even more so at higher rates. Thus, we show that the market-based solution is a robust task assignment algorithm.