An island-based coevolutionary algorithm with mixed Nash equilibrium weighting builds robust decision tree ensembles that outperform prior methods on 20 benchmarks.
An experimental analysis of Lemke-Howson algorithm
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abstract
We present an experimental investigation of the performance of the Lemke-Howson algorithm, which is the most widely used algorithm for the computation of a Nash equilibrium for bimatrix games. Lemke-Howson algorithm is based upon a simple pivoting strategy, which corresponds to following a path whose endpoint is a Nash equilibrium. We analyze both the basic Lemke-Howson algorithm and a heuristic modification of it, which we designed to cope with the effects of a 'bad' initial choice of the pivot. Our experimental findings show that, on uniformly random games, the heuristics achieves a linear running time, while the basic Lemke-Howson algorithm runs in time roughly proportional to a polynomial of degree seven. To conduct the experiments, we have developed our own implementation of Lemke-Howson algorithm, which turns out to be significantly faster than state-of-the-art software. This allowed us to run the algorithm on a much larger set of data, and on instances of much larger size, compared with previous work.
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cs.LG 1years
2024 1verdicts
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Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
An island-based coevolutionary algorithm with mixed Nash equilibrium weighting builds robust decision tree ensembles that outperform prior methods on 20 benchmarks.