Abstract
We present an evolutionary algorithm evo-SMC for the problem of Submodular Maximization under Cost constraints (SMC). Our algorithm achieves 1/2-approximation with a high probability 1-1/n within O(n2 K) iterations, where K denotes the maximum size of a feasible solution set with cost constraint B. To the best of our knowledge, this is the best approximation guarantee offered by evolutionary algorithms for this problem. We further refine evo-SMC and develop st-evo-SMC. This stochastic version yields a significantly faster algorithm while maintaining the approximation ratio of 1/2, with probability 1-epsilon. The required number of iterations reduces to O(nK log(1/epsilon)/p), where the user defined parameters p represents the stochasticity probability, and epsilon denotes the error threshold. Finally, the empirical evaluations carried out through extensive experimentation substantiate the efficiency and effectiveness of our proposed algorithms. Our algorithms consistently outperform existing methods, producing higher-quality solutions.
Keywords
- Search: S: Evolutionary computation
- Machine Learning: ML: Optimization
- Search: S: Combinatorial search and optimisation
- Machine Learning: ML: Evolutionary learning
