What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Mathematical optimization, Genetic algorithm, Artificial intelligence, Machine learning and Algorithm.
His work on Population-based incremental learning and Tournament selection as part of general Mathematical optimization research is frequently linked to Sizing, thereby connecting diverse disciplines of science.
His research integrates issues of Function, Multi-objective optimization, Optimization problem and Theoretical computer science in his study of Genetic algorithm.
His research on Artificial intelligence frequently connects to adjacent areas such as Deception.
His Machine learning research incorporates themes from Class and Probabilistic logic.
His Genetic representation study combines topics in areas such as Computer programming, Linkage learning, Pascal and Cultural algorithm.
His most cited work include:
- Genetic algorithms in search, optimization and machine learning (30025 citations)
- Genetic algorithms in search, optimization, and machine learning (15227 citations)
- Genetic Algorithms in Search (10096 citations)
What are the main themes of his work throughout his whole career to date?
David E. Goldberg mainly investigates Genetic algorithm, Mathematical optimization, Artificial intelligence, Machine learning and Algorithm.
The Genetic algorithm study combines topics in areas such as Evolutionary computation, Theoretical computer science and Crossover.
In the field of Mathematical optimization, his study on Estimation of distribution algorithm, Evolutionary algorithm, Meta-optimization and Tournament selection overlaps with subjects such as Sizing.
His work deals with themes such as Probabilistic logic and Statistical model, which intersect with Estimation of distribution algorithm.
His studies in Tournament selection integrate themes in fields like Fitness proportionate selection and Truncation selection.
His Genetic representation research incorporates elements of Quality control and genetic algorithms and Cultural algorithm.
He most often published in these fields:
- Genetic algorithm (36.82%)
- Mathematical optimization (32.27%)
- Artificial intelligence (32.50%)
What were the highlights of his more recent work (between 2007-2021)?
- Artificial intelligence (32.50%)
- Machine learning (24.55%)
- Genetic algorithm (36.82%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Genetic algorithm, Estimation of distribution algorithm and Classifier.
Many of his research projects under Artificial intelligence are closely connected to Sizing with Sizing, tying the diverse disciplines of science together.
His study of Bayesian network is a part of Machine learning.
David E. Goldberg has included themes like Minimum description length, Algorithm, Theoretical computer science, Cluster analysis and Design structure matrix in his Genetic algorithm study.
His research in Estimation of distribution algorithm intersects with topics in Evolutionary algorithm, Evolutionary computation and Statistical model.
His work carried out in the field of Classifier brings together such families of science as Training set, Gene expression programming, Binary number, Feature extraction and Genetic programming.
Between 2007 and 2021, his most popular works were:
- Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms (161 citations)
- Scaling Genetic Algorithms Using MapReduce (143 citations)
- The crowding approach to niching in genetic algorithms (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Machine learning, Artificial intelligence, Estimation of distribution algorithm, Evolutionary algorithm and Statistical model are his primary areas of study.
His Machine learning study focuses on Bayesian network in particular.
Particularly relevant to Reinforcement learning is his body of work in Artificial intelligence.
His study in Estimation of distribution algorithm is interdisciplinary in nature, drawing from both Theoretical computer science and Probabilistic analysis of algorithms.
David E. Goldberg combines subjects such as Sampling, Function and Class with his study of Statistical model.
His study in the field of Cultural algorithm also crosses realms of Running time.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
