Multi-objective evolutionary algorithms are generally good: Maximizing monotone submodular functions over sequences.
Chao QianDan-Xuan LiuChao FengKe TangPublished in: Theor. Comput. Sci. (2023)
Keyphrases
- submodular functions
- multi objective evolutionary algorithms
- greedy algorithm
- facility location problem
- multi objective
- multi objective optimization
- combinatorial optimization
- objective function
- energy function
- test problems
- nsga ii
- convex optimization
- multi criteria
- fitness function
- branch and bound algorithm
- evolutionary computation
- evolutionary algorithm
- learning problems
- worst case
- genetic algorithm
- multiobjective optimization
- lower bound
- learning algorithm