Search Results for author:
Found 6 papers, 4 papers with code
Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization
In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO).
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions.
Automated Design of Metaheuristic Algorithms: A Survey
Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality.
AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO).
Representation Learning for Heterogeneous Information Networks via Embedding Events
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.
Ranked #1 on
Link Prediction
on IMDb
