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We introduce a load balanced adaptive routing algorithm for torus networks, GOAL - globally oblivious adaptive locally - that provides high throughput on adversarial traffic patterns, matching or exceeding fully randomized routing and exceeding the worst case performance of Chaos (K. Bolding et al., 1997), RLB (A. Singh et al., 2002), and minimal routing (L. Gravano et al., 1994) by more than 40%. GOAL also preserves locality to provide up to 4.6* the throughput of fully randomized routing (L. G. Valiant, 1982) on local traffic. GOAL achieves global load balance by randomly choosing the direction to route in each dimension. Local load balance is then achieved by routing in the selected directions adaptively. We compare the throughput, latency, stability and hot spot performance of GOAL to six previously published routing algorithms on six specific traffic patterns and 1000 randomly generated permutations.