What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Genetic programming, Artificial intelligence, Theoretical computer science, Machine learning and Evolutionary algorithm.
His Genetic programming research is multidisciplinary, relying on both Programming language and Genetic algorithm, Evolutionary programming.
Wolfgang Banzhaf has included themes like Cryptography, Information hiding, Set, Steganography and DNA computing in his Theoretical computer science study.
His research investigates the link between Machine learning and topics such as Machine code that cross with problems in Pointer, Executable and Source code.
His Evolutionary algorithm study combines topics in areas such as Data type, Floating point and Face.
His Linear genetic programming study combines topics from a wide range of disciplines, such as Field, Code, Linear programming, Software and Programming paradigm.
His most cited work include:
- Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications (1502 citations)
- Genetic Programming: An Introduction (954 citations)
- Genetic and Evolutionary Computation - GECCO 2004 (887 citations)
What are the main themes of his work throughout his whole career to date?
Wolfgang Banzhaf mainly focuses on Genetic programming, Artificial intelligence, Theoretical computer science, Evolutionary algorithm and Machine learning.
His study focuses on the intersection of Genetic programming and fields such as Genetic algorithm with connections in the field of Crossover.
Many of his studies on Artificial intelligence apply to Process as well.
His Theoretical computer science study integrates concerns from other disciplines, such as Set and Cartesian genetic programming.
The subject of his Evolutionary algorithm research is within the realm of Mathematical optimization.
His Linear genetic programming research incorporates themes from Evolvability and Econometrics.
He most often published in these fields:
- Genetic programming (45.10%)
- Artificial intelligence (34.72%)
- Theoretical computer science (18.10%)
What were the highlights of his more recent work (between 2017-2021)?
- Genetic programming (45.10%)
- Artificial intelligence (34.72%)
- Evolutionary algorithm (17.80%)
In recent papers he was focusing on the following fields of study:
Wolfgang Banzhaf mainly investigates Genetic programming, Artificial intelligence, Evolutionary algorithm, Machine learning and Artificial neural network.
The study incorporates disciplines such as Tournament selection, Selection, Tournament and Computational complexity theory, Algorithm in addition to Genetic programming.
His Artificial intelligence research is multidisciplinary, incorporating elements of Task and Search algorithm.
His research in Evolutionary algorithm intersects with topics in Variation, Theoretical computer science, Evolvability, Computational biology and Process.
The Variation study which covers Algorithm design that intersects with Linear genetic programming.
He has researched Artificial neural network in several fields, including Genetic algorithm, Biological system, DNA sequencing and Receiver operating characteristic.
Between 2017 and 2021, his most popular works were:
- NSGA-Net: neural architecture search using multi-objective genetic algorithm (93 citations)
- ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming (36 citations)
- NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search. (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Evolutionary algorithm, Genetic programming, Machine learning and Benchmark.
His research on Artificial intelligence frequently connects to adjacent areas such as Search algorithm.
His study explores the link between Evolutionary algorithm and topics such as Computational biology that cross with problems in Neutral network, Evolvability, Neutrality, Implementation and Expression.
His work carried out in the field of Genetic programming brings together such families of science as Programming language, Metaheuristic, Global optimization, Test suite and Evolutionary computation.
Many of his research projects under Machine learning are closely connected to Hybrid with Hybrid, tying the diverse disciplines of science together.
The study incorporates disciplines such as Transfer of learning, Symbolic regression, Interpretability and Feature engineering in addition to Benchmark.
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