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Recently we have seen an emergent trend of self adaptive routing in both Internet and wireless ad hoc networks. Although there are previous methods for computing the traffic equilibria of self adaptive routing (e.g., selfish routing), these methods use computationally demanding algorithms and require that a precise analytical model of the network be given. Also, it remains an open question how to design an adaptive routing scheme which ensures convergence to traffic equilibria in practice. In this paper we propose a simple, efficient, distributed probabilistic routing scheme for self adaptive routing in dynamic, realistic environments. Using both analysis and extensive simulations, we show that our scheme can converge to the desired traffic equilibrium (either user-optimal or network-optimal) very quickly. We find that user-optimal routing can achieve very close to optimal average latency in dynamic environments, but such performance often comes at the cost of seriously overloading certain links. To avoid link overloads, we improve adaptive routing by optimizing average user latency and link utilization simultaneously. Our evaluation shows that there is a trade-off between optimizing dual objectives, but the degradation in average latency is only marginal for typical link utilization requirements.