Erick Cantú‐Paz
Impact in
- Artificial Intelligence top 0.5%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Neural Networks and Applications
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- Computational Theory and Mathematics top 0.5%
- Advanced Multi-Objective Optimization Algorithms
Papers in
-
- Metaheuristic Optimization Algorithms Research 22
- Evolutionary Algorithms and Applications 22
- Machine Learning and Data Classification 3
- Neural Networks and Applications 3
- Bayesian Modeling and Causal Inference 2
-
- Advanced Multi-Objective Optimization Algorithms 9
- Co-authors
- David E. GoldbergMartin PelikánChandrika KamathBrad L. MillerKumara SastryHaibin ChengShawn NewsamDaria Sorokina
- Journals
- Evolutionary Computation (3 papers)Journal of Parallel and Distributed Computing (1 paper)Soft Computing (1 paper)Neural Networks (1 paper)Computer Methods in Applied Mechanics and Engineering (1 paper)
- Partner nations
- United StatesSpainUnited Kingdom
In The Last Decade
Erick Cantú‐Paz
46 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Artificial Intelligence 2.4k
- Computational Theory and Mathematics 1.0k
- Industrial and Manufacturing Engineering 293
- Management Science and Operations Research 216
- Computer Networks and Communications 379
Countries citing papers authored by Erick Cantú‐Paz
This map shows the geographic impact of Erick Cantú‐Paz'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 Erick Cantú‐Paz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erick Cantú‐Paz more than expected).
Fields of papers citing papers by Erick Cantú‐Paz
This network shows the impact of papers produced by Erick Cantú‐Paz. 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 Erick Cantú‐Paz. The network helps show where Erick Cantú‐Paz may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Erick Cantú‐Paz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 12 | |
| 2 | Scalable optimization via probabilistic modeling : from algorithms to applications | 2006 | 87 |
| 3 | Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) | 2006 | 70 |
| 4 | 2005 | 102 | |
| 5 | Genetic and evolutionary computation - GECCO 2003 : Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 : proceedings | 2003 | 1 |
| 6 | 2003 | 11 | |
| 7 | On Random Numbers And The Performance Of Genetic Algorithms | 2002 | 32 |
| 8 | Feature Subset Selection by Estimation of Distribution Algorithms | 2002 | 23 |
| 9 | Evolving Neural Networks for the Classification of Galaxies | 2002 | 9 |
| 10 | 2002 | 16 | |
| 11 | Supervised and unsupervised discretization methods for evolutionary algorithms | 2001 | 17 |
| 12 | Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence | 2000 | 59 |
| 13 | Selection intensity in genetic algorithms with generation gaps | 2000 | 4 |
| 14 | Using Evolutionary Algorithms to Induce Oblique Decision Trees | 2000 | 25 |
| 15 | A Survey of Parallel Genetic Algorithms Hit paper breakdown → | 2000 | 580 |
| 16 | 2000 | 43 | |
| 17 | BOA: the Bayesian optimization algorithm Hit paper breakdown → | 1999 | 627 |
| 18 | Topologies, migration rates, and multi-population parallel genetic algorithms | 1999 | 60 |
| 19 | Migration policies and takeover times in genetic algorithms | 1999 | 16 |
| 20 | Predicting Speedups of Ideal Bounding Cases of Parallel Genetic Algorithms. | 1997 | 33 |
About Erick Cantú‐Paz
Erick Cantú‐Paz is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Information Systems, having authored 46 papers that have together received 3.6k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (22 papers), Evolutionary Algorithms and Applications (22 papers), Advanced Multi-Objective Optimization Algorithms (9 papers), Machine Learning and Data Classification (3 papers), Data Mining Algorithms and Applications (3 papers), Neural Networks and Applications (3 papers), Recommender Systems and Techniques (3 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Computational Theory and Mathematics (1.0k citations), Industrial and Manufacturing Engineering (293 citations), Management Science and Operations Research (216 citations) and Computer Networks and Communications (379 citations). Erick Cantú‐Paz has collaborated with scholars based in United States, Spain and United Kingdom. Frequent co-authors include David E. Goldberg, Martin Pelikán, Chandrika Kamath, Brad L. Miller, Kumara Sastry, Haibin Cheng, Shawn Newsam, Daria Sorokina, Eren Manavoglu and Wanhong Xu. Their work appears in journals such as Evolutionary Computation, Journal of Parallel and Distributed Computing, Soft Computing, Neural Networks and Computer Methods in Applied Mechanics and Engineering.
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.