Erik D. Goodman

9.3k citations
198 papers · 6.0k indexed · 5 hit papers · h-index 37
Topics
Evolutionary Algorithms and Applications (74 papers)Metaheuristic Optimization Algorithms Research (71 papers)Advanced Multi-Objective Optimization Algorithms (55 papers)

In The Last Decade

Erik D. Goodman

188 papers receiving 5.7k citations

Hit Papers

Dimensionality reduction using genetic algorithms200020262008201720002009201820192019200400600

Peers

Erik D. Goodman
Comparison fields: 5 of 180
  • Artificial Intelligence 3.1k
  • Computational Theory and Mathematics 2.0k
  • Computer Vision and Pattern Recognition 771
  • Control and Systems Engineering 588
  • Industrial and Manufacturing Engineering 485
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Countries citing papers authored by Erik D. Goodman

Since Specialization
Citations

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

Fields of papers citing papers by Erik D. Goodman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erik D. Goodman. 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 Erik D. Goodman. The network helps show where Erik D. Goodman may publish in the future.

Co-authorship network of co-authors of Erik D. Goodman

This figure shows the co-authorship network connecting the top 25 collaborators of Erik D. Goodman. A scholar is included among the top collaborators of Erik D. Goodman 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 Erik D. Goodman. Erik D. Goodman 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
#WorkIndexed citations
1 2
2 2
3 2
4 9
5 16
6 8
7 1
8 4
9 12
10
Towards an Evolvable Chromosome Model for Interactive Computer Design Support
1
11
Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
28
12
Exploring Multiple Design Topologies Using Genetic Programming And Bond Graphs
6
13
Structure Fitness Sharing (SFS) for evolutionary design by genetic programming
12
14
First steps toward automated design of mechatronic systems using bond graphs and genetic programming
5
15
A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem.
55
16 121
17
A Standard GA Approach to Native Protein Conformation Prediction
48
18
Further Research on Feature Selection and Classification Using Genetic Algorithms
167
19
Genetic learning procedures in distributed environments
11
20 12

About Erik D. Goodman

Erik D. Goodman is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 198 papers that have together received 6.0k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (74 papers), Metaheuristic Optimization Algorithms Research (71 papers) and Advanced Multi-Objective Optimization Algorithms (55 papers). The work is most often cited by research in Computational Theory and Mathematics (2.0k citations), Artificial Intelligence (3.1k citations) and Industrial and Manufacturing Engineering (485 citations). Erik D. Goodman has collaborated with scholars based in United States, China and Denmark. Frequent co-authors include William F. Punch, Lihong Xu, Kalyanmoy Deb, Zhun Fan, Michael L. Raymer, Leslie A. Kuhn, Anil K. Jain, Zhichao Lu, Xinye Cai and Wenji Li. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Molecular Biology and Ecology.

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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