David E. Goldberg
Impact in
- Artificial Intelligence top 0.01%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Computational Theory and Mathematics top 0.01%
- Advanced Multi-Objective Optimization Algorithms
Papers in
-
- Metaheuristic Optimization Algorithms Research 208
- Evolutionary Algorithms and Applications 196
- Bayesian Modeling and Causal Inference 15
- Machine Learning and Data Classification 14
- Neural Networks and Applications 12
-
- Advanced Multi-Objective Optimization Algorithms 83
- Co-authors
- William Shakespeare (1 shared paper)Kalyanmoy Deb (19 shared papers)John H. Holland (3 shared papers)Martin Pelikán (45 shared papers)Jeffrey Horn (8 shared papers)Kumara Sastry (77 shared papers)Fernando G. Lobo (15 shared papers)Erick Cantú‐Paz (11 shared papers)
- Journals
- Evolutionary Computation (19 papers)Lecture notes in computer science (51 papers)IEEE Transactions on Evolutionary Computation (4 papers)Inorganic Chemistry (3 papers)The Journal of Physical Chemistry (3 papers)
- Partner nations
- United StatesGermanyPortugal
In The Last Decade
David E. Goldberg
388 papers receiving 54.7k citations
David E. Goldberg's Hit Papers
Peers
Comparison fields: 5 of 226
- Artificial Intelligence 28.3k
- Computational Theory and Mathematics 12.9k
- Industrial and Manufacturing Engineering 5.5k
- Management Science and Operations Research 3.4k
- Control and Systems Engineering 5.8k
Countries citing papers authored by David E. Goldberg
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
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-authors
The 25 scholars most cited alongside David E. Goldberg, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 399 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Genetic Algorithms in Search, Optimization and Machine Learning Hit paper breakdown → | 1988 | 14726 |
| 2 | Genetic Algorithms Hit paper breakdown → | 2002 | 12799 |
| 3 | Genetic Algorithms and Machine Learning Hit paper breakdown → | 1988 | 2484 |
| 4 | A Comparative Analysis of Selection Schemes Used in Genetic Algorithms Hit paper breakdown → | 1991 | 1852 |
| 5 | A niched Pareto genetic algorithm for multiobjective optimization Hit paper breakdown → | 2002 | 1810 |
| 6 | Genetic algorithms with sharing for multimodal function optimization Hit paper breakdown → | 1987 | 1555 |
| 7 | Messy genetic algorithms: motivation, analysis, and first results Hit paper breakdown → | 1989 | 959 |
| 8 | BOA: the Bayesian optimization algorithm Hit paper breakdown → | 1999 | 774 |
| 9 | Genetic Algorithms, Tournament Selection, and the Effects of Noise. Hit paper breakdown → | 1995 | 755 |
| 10 | The compact genetic algorithm Hit paper breakdown → | 1999 | 752 |
| 11 | The Design of Innovation: Lessons from and for Competent Genetic Algorithms Hit paper breakdown → | 2002 | 738 |
| 12 | An Investigation of Niche and Species Formation in Genetic Function Optimization Hit paper breakdown → | 1989 | 684 |
| 13 | A Survey of Optimization by Building and Using Probabilistic Models Hit paper breakdown → | 2002 | 611 |
| 14 | Alleles, loci and the traveling salesman problem Hit paper breakdown → | 1985 | 567 |
| 15 | Genetic algorithms, noise, and the sizing of populations | 1991 | 481 |
| 16 | 2002 | 394 | |
| 17 | Sizing Populations for Serial and Parallel Genetic Algorithms | 1989 | 392 |
| 18 | Real-coded genetic algorithms. Virtual alphabets, and blocking. | 1991 | 332 |
| 19 | 1986 | 290 | |
| 20 | 1993 | 282 |
About David E. Goldberg
David E. Goldberg is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology, Ocean Engineering and Organic Chemistry, having authored 399 papers that have together received 60.0k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (208 papers), Evolutionary Algorithms and Applications (196 papers), Advanced Multi-Objective Optimization Algorithms (83 papers), Viral Infectious Diseases and Gene Expression in Insects (19 papers), Bayesian Modeling and Causal Inference (15 papers), Water resources management and optimization (15 papers), Machine Learning and Data Classification (14 papers) and Neural Networks and Applications (12 papers). The work is most often cited by research in Artificial Intelligence (28.3k citations), Computational Theory and Mathematics (12.9k citations), Industrial and Manufacturing Engineering (5.5k citations), Management Science and Operations Research (3.4k citations) and Control and Systems Engineering (5.8k citations). David E. Goldberg has collaborated with scholars based in United States, Germany and Portugal. Frequent co-authors include William Shakespeare, Kalyanmoy Deb, John H. Holland, Martin Pelikán, Jeffrey Horn, Kumara Sastry, Fernando G. Lobo, Erick Cantú‐Paz, Brad L. Miller and R. Lingle. Their work appears in journals such as Evolutionary Computation, Lecture notes in computer science, IEEE Transactions on Evolutionary Computation, Inorganic Chemistry and The Journal of Physical Chemistry.
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.