97 citations · 287 across the 26 of their papers we have counts for
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A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Peng Yang, Ke Tang, Xin Yao
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-…
Analysis of Noisy Evolutionary Optimization When Sampling Fails
Chao Qian, Chao Bian, Yang Yu +2
In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{…
Multiobjective Test Problems with Degenerate Pareto Fronts
Liangli Zhen, Miqing Li, Ran Cheng +2
In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation alg…
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Shengcai Liu, Ke Tang, Xin Yao
Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems. However, manually building such solver portfoli…
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
Ke Li, Renzhi Chen, Dragan Savic +1
Decomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approxima…