David E. Goldberg

74.2k total citations · 13 hit papers
305 papers, 46.6k citations indexed

About

David E. Goldberg is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, David E. Goldberg has authored 305 papers receiving a total of 46.6k indexed citations (citations by other indexed papers that have themselves been cited), including 173 papers in Artificial Intelligence, 65 papers in Computational Theory and Mathematics and 29 papers in Molecular Biology. Recurrent topics in David E. Goldberg's work include Metaheuristic Optimization Algorithms Research (140 papers), Evolutionary Algorithms and Applications (134 papers) and Advanced Multi-Objective Optimization Algorithms (60 papers). David E. Goldberg is often cited by papers focused on Metaheuristic Optimization Algorithms Research (140 papers), Evolutionary Algorithms and Applications (134 papers) and Advanced Multi-Objective Optimization Algorithms (60 papers). David E. Goldberg collaborates with scholars based in United States, Germany and Portugal. David E. Goldberg's co-authors include William Shakespeare, John H. Holland, Kalyanmoy Deb, Martin Pelikán, Jeffrey Horn, Fernando G. Lobo, Brad L. Miller, Erick Cantú‐Paz, Kumara Sastry and R. Lingle and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review B.

In The Last Decade

David E. Goldberg

296 papers receiving 42.1k citations

Hit Papers

Genetic Algorithms in Search, Optimization and Machine Le... 1985 2026 1998 2012 1988 2002 1988 2002 1987 4.0k 8.0k 12.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David E. Goldberg United States 71 19.9k 9.4k 5.1k 4.7k 4.4k 305 46.6k
John H. Holland United States 33 14.3k 0.7× 6.0k 0.6× 3.6k 0.7× 3.9k 0.8× 4.0k 0.9× 63 41.0k
Xin‐She Yang United Kingdom 67 16.7k 0.8× 7.6k 0.8× 6.7k 1.3× 4.6k 1.0× 2.8k 0.6× 325 34.1k
Amrit Pratap United States 4 9.6k 0.5× 10.7k 1.1× 5.7k 1.1× 4.1k 0.9× 4.4k 1.0× 5 34.1k
Scott Kirkpatrick United States 46 10.2k 0.5× 5.6k 0.6× 6.9k 1.3× 2.3k 0.5× 4.8k 1.1× 117 50.9k
Kenneth V. Price Germany 12 14.2k 0.7× 8.4k 0.9× 5.3k 1.0× 4.1k 0.9× 1.9k 0.4× 13 29.8k
Jun Zhang China 103 17.8k 0.9× 10.0k 1.1× 7.3k 1.4× 4.1k 0.9× 3.2k 0.7× 2.7k 56.5k
Xin Yao China 95 28.9k 1.5× 14.6k 1.5× 4.3k 0.8× 3.3k 0.7× 3.2k 0.7× 875 44.3k
Rainer Storn Germany 11 13.3k 0.7× 7.9k 0.8× 5.2k 1.0× 3.9k 0.8× 1.6k 0.4× 15 28.3k
Carlos A. Coello Coello Mexico 76 19.2k 1.0× 18.5k 2.0× 4.1k 0.8× 4.1k 0.9× 3.1k 0.7× 484 34.0k
Ponnuthurai Nagaratnam Suganthan Singapore 104 30.5k 1.5× 18.3k 1.9× 8.6k 1.7× 6.2k 1.3× 5.1k 1.2× 556 50.6k

Countries citing papers authored by David E. Goldberg

Since Specialization
Citations

This map shows the geographic impact of David E. Goldberg'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 David E. Goldberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David E. Goldberg more than expected).

Fields of papers citing papers by David E. Goldberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David E. Goldberg. 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 David E. Goldberg. The network helps show where David E. Goldberg may publish in the future.

Co-authorship network of co-authors of David E. Goldberg

This figure shows the co-authorship network connecting the top 25 collaborators of David E. Goldberg. A scholar is included among the top collaborators of David E. Goldberg 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 David E. Goldberg. David E. Goldberg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Verma, Abhishek, Xavier Llorà, Shivaram Venkataraman, David E. Goldberg, & Roy H. Campbell. (2010). Scaling eCGA model building via data-intensive computing. 1–8. 20 indexed citations
2.
Llorà, Xavier, et al.. (2007). Analyzing Trends in the Blogosphere Using Human-Centered Analysis and Visualization Tools. 2 indexed citations
3.
Sastry, Kumara & David E. Goldberg. (2002). Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Variances. Genetic and Evolutionary Computation Conference. 528–535. 3 indexed citations
4.
Pelikán, Martin & David E. Goldberg. (2001). Escaping hierarchical traps with competent genetic algorithms. Genetic and Evolutionary Computation Conference. 511–518. 102 indexed citations
5.
Albert, Laura A. & David E. Goldberg. (2001). Efficient evaluation relaxation under integrated fitness functions. Genetic and Evolutionary Computation Conference. 751–751. 2 indexed citations
6.
Reed, Patrick M., Barbara Minsker, & David E. Goldberg. (2001). Designing a new elitist Nondominated Sorted Genetic Algorithm for a multiobjective long term groundwater monitoring application. Genetic and Evolutionary Computation Conference. 1454–1454. 4 indexed citations
7.
Espinoza, Felipe, Barbara Minsker, & David E. Goldberg. (2001). A self adaptive hybrid genetic algorithm. Genetic and Evolutionary Computation Conference. 759–759. 23 indexed citations
8.
Pelikán, Martin, David E. Goldberg, & Erick Cantú‐Paz. (2000). Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence. University of North Texas Digital Library (University of North Texas). 59 indexed citations
9.
Goldberg, David E.. (1999). Schaum's outline of theory and problems of beginning chemistry. McGraw-Hill eBooks. 1 indexed citations
10.
Goldberg, David E., et al.. (1997). Takeover Time in a Noisy Environment.. 160–165. 2 indexed citations
11.
Miller, Brad L. & David E. Goldberg. (1995). Genetic Algorithms, Tournament Selection, and the Effects of Noise.. Complex Systems. 9. 665 indexed citations breakdown →
12.
Deb, Kalyanmoy, Jeffrey Horn, & David E. Goldberg. (1993). Multimodal Deceptive Functions.. Complex Systems. 7. 31 indexed citations
13.
Smith, Robert E. & David E. Goldberg. (1992). Diploidy and Dominance in Artificial Genetic Search.. Complex Systems. 6. 29 indexed citations
14.
Benekohal, Rahim F., et al.. (1992). SIGNAL TIMING DETERMINATION USING GENETIC ALGORITHMS. Transportation Research Record Journal of the Transportation Research Board. 99 indexed citations
15.
Goldberg, David E.. (1990). A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing.. Complex Systems. 4. 131 indexed citations
16.
Goldberg, David E.. (1989). Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis.. Complex Systems. 3. 112 indexed citations
17.
Wilson, Stewart W. & David E. Goldberg. (1989). A critical review of classifier systems. international conference on Genetic algorithms. 244–255. 78 indexed citations
18.
Goldberg, David E.. (1989). Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction.. Complex Systems. 3. 136 indexed citations
19.
Goldberg, David E.. (1989). Zen and the Art of Genetic Algorithms. international conference on Genetic algorithms. 80–85. 57 indexed citations
20.
Goldberg, David E., et al.. (1987). An analysis of reproduction and crossover in a binary-coded genetic algorithm. international conference on Genetic algorithms. 9–13. 48 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.

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