activity
20132022
most citedImproved Lower Bounds for Sum Coloring via Clique Decomposition

10 citations · 28 across the 9 of their papers we have counts for

collaborators

12 papers

cs.NE2022

Heuristic Search for Rank Aggregation with Application to Label Ranking

Yangming Zhou, Jin-Kao Hao, Zhen Li +1

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of pract…

cs.NE2019

Variable Population Memetic Search: A Case Study on the Critical Node Problem

Yangming Zhou, Jin-Kao Hao, Zhang-Hua Fu +2

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to…

cs.LG2019

Population-based Gradient Descent Weight Learning for Graph Coloring Problems

Olivier Goudet, Béatrice Duval, Jin-Kao Hao

Graph coloring involves assigning colors to the vertices of a graph such that two vertices linked by an edge receive different colors. Graph coloring problems are general models th…

cs.AI20192 cited

Iterated two-phase local search for the Set-Union Knapsack Problem

Zequn Wei, Jin-Kao Hao

The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1 knapsack problem. Given a set of weighted elements and a set of items with profits where each item is c…

cs.DM20173 cited

When data mining meets optimization: A case study on the quadratic assignment problem

Yangming Zhou, Jin-Kao Hao, Béatrice Duval

This paper presents a hybrid approach called frequent pattern based search that combines data mining and optimization. The proposed method uses a data mining procedure to mine freq…

cs.AI20173 cited

Combining tabu search and graph reduction to solve the maximum balanced biclique problem

Yi Zhou, Jin-Kao Hao

The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains. This paper introduces a novel algorithm, which combines an effectiv…