Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A multi-objective genetic local search algorithm and its application to flowshop scheduling
Countries citing papers authored by Tadahiko Murata
Since
Specialization
Citations
This map shows the geographic impact of Tadahiko Murata's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tadahiko Murata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tadahiko Murata more than expected).
This network shows the impact of papers produced by Tadahiko Murata. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tadahiko Murata. The network helps show where Tadahiko Murata may publish in the future.
Co-authorship network of co-authors of Tadahiko Murata
This figure shows the co-authorship network connecting the top 25 collaborators of Tadahiko Murata.
A scholar is included among the top collaborators of Tadahiko Murata based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Tadahiko Murata. Tadahiko Murata is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Murata, Tadahiko, et al.. (2001). Three-objective genetic algorithms for designing compact fuzzy rule-based systems for pattern classification problems. Genetic and Evolutionary Computation Conference. 485–492.9 indexed citations
16.
Murata, Tadahiko, Hisao Ishibuchi, & Mitsuo Gen. (2000). Cellular genetic local search for multi-objective optimization. Genetic and Evolutionary Computation Conference. 307–314.23 indexed citations
17.
Ishibuchi, Hisao, et al.. (1997). Effectiveness of Genetic Local Search Algorithms.. 505–512.10 indexed citations
18.
Ishibuchi, Hisao, et al.. (1997). Flowshop Scheduling by Genetic Local Search. Transactions of the Institute of Systems Control and Information Engineers. 10(10). 563–565.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.