What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Mathematical optimization
Mathematical optimization, Tabu search, Algorithm, Graph coloring and Local search are his primary areas of study.
His Mathematical optimization study frequently draws connections to other fields, such as Benchmark.
Jin-Kao Hao has researched Tabu search in several fields, including Hybrid algorithm, Set, Knapsack problem and Search algorithm.
His Algorithm research includes themes of Consistency, Heuristics and Cluster analysis.
His studies in Graph coloring integrate themes in fields like Theoretical computer science, Greedy coloring, List coloring and Chordal graph.
His Local search study combines topics in areas such as Maximum cut and Metaheuristic.
His most cited work include:
- Hybrid Evolutionary Algorithms for Graph Coloring (414 citations)
- Transit network design and scheduling: A global review (405 citations)
- Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information (201 citations)
What are the main themes of his work throughout his whole career to date?
Jin-Kao Hao mainly investigates Mathematical optimization, Tabu search, Algorithm, Local search and Benchmark.
His Mathematical optimization research incorporates elements of Set and Crossover.
In his research on the topic of Tabu search, Iterated function is strongly related with Local optimum.
His research in Algorithm intersects with topics in Path, Evaluation function and Genetic algorithm.
He interconnects Evolutionary algorithm and Vertex in the investigation of issues within Local search.
His Benchmark study also includes
- Theoretical computer science, which have a strong connection to Graph coloring,
- Graph which is related to area like Partition.
He most often published in these fields:
- Mathematical optimization (68.36%)
- Tabu search (48.79%)
- Algorithm (30.83%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (68.36%)
- Tabu search (48.79%)
- Benchmark (30.03%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Mathematical optimization, Tabu search, Benchmark, Local search and Graph.
His Mathematical optimization study frequently links to adjacent areas such as Crossover.
His Tabu search research incorporates elements of Metaheuristic, Heuristics, Evolutionary algorithm, Combinatorial optimization and Bipartite graph.
His study in Benchmark is interdisciplinary in nature, drawing from both Theoretical computer science, Set, Code, Key and Search algorithm.
His Local search study is associated with Algorithm.
His research investigates the connection with Algorithm and areas like Conductance which intersect with concerns in Complex network and Quotient.
Between 2018 and 2021, his most popular works were:
- Two-stage solution-based tabu search for the multidemand multidimensional knapsack problem (18 citations)
- Two-stage solution-based tabu search for the multidemand multidimensional knapsack problem (18 citations)
- Memetic Search for Identifying Critical Nodes in Sparse Graphs (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
Jin-Kao Hao mostly deals with Mathematical optimization, Tabu search, Benchmark, Local search and Metaheuristic.
His Mathematical optimization study frequently draws connections between related disciplines such as Crossover.
He combines subjects such as Partition, Disjoint sets, Memetic algorithm, Evolutionary algorithm and Vertex with his study of Tabu search.
His Partition research incorporates themes from Conductance, Algorithm and Graph partition.
His Benchmark research is multidisciplinary, incorporating perspectives in Theoretical computer science, Combinatorial optimization and Search algorithm.
His biological study spans a wide range of topics, including Variable and Routing.
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