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Found 3 papers, 1 papers with code
Migrant Resettlement by Evolutionary Multi-objective Optimization
In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem.
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i. e., the greedy algorithm and local search) under both static and dynamic environments.
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution.
