What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Programming language
His primary areas of study are Evolutionary algorithm, Theoretical computer science, Genetic programming, Artificial intelligence and Genetic algorithm.
The study incorporates disciplines such as Field, Evolvability and Face in addition to Evolutionary algorithm.
His Theoretical computer science research includes elements of Evolutionary computation, Evolvable hardware, Graph and Source code.
His biological study focuses on Genetic representation.
The concepts of his Genetic representation study are interwoven with issues in Factor and Evolutionary programming.
His Artificial intelligence study integrates concerns from other disciplines, such as Routing, Digital electronics and Fitness landscape.
His most cited work include:
- Cartesian Genetic Programming (795 citations)
- Principles in the Evolutionary Design of Digital Circuits—Part II (382 citations)
- Genetic and Evolutionary Computation -- GECCO-2003 (368 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Genetic programming, Artificial intelligence, Theoretical computer science, Evolutionary algorithm and Cartesian genetic programming.
The Genetic programming study combines topics in areas such as Algorithm and Genetic algorithm, Mathematical optimization, Evolutionary programming.
His research in the fields of Artificial neural network and Representation overlaps with other disciplines such as Context.
His work in Theoretical computer science covers topics such as Evolutionary computation which are related to areas like Electronic component and Distributed computing.
His Evolutionary algorithm study which covers Computation that intersects with Physical system.
Fault tolerance and Fitness landscape is closely connected to Digital electronics in his research, which is encompassed under the umbrella topic of Evolvable hardware.
He most often published in these fields:
- Genetic programming (30.86%)
- Artificial intelligence (30.45%)
- Theoretical computer science (27.16%)
What were the highlights of his more recent work (between 2014-2020)?
- Theoretical computer science (27.16%)
- Genetic programming (30.86%)
- Evolutionary algorithm (21.81%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Theoretical computer science, Genetic programming, Evolutionary algorithm, Cartesian genetic programming and Computational problem.
His studies in Theoretical computer science integrate themes in fields like Boolean function, Series and Encoding.
In the subject of general Genetic programming, his work in Genetic representation is often linked to Weight factor, thereby combining diverse domains of study.
His Evolutionary algorithm study integrates concerns from other disciplines, such as Algorithm, Computation, Regularization and Reservoir computing.
The concepts of his Cartesian genetic programming study are interwoven with issues in Artificial neural network, Multimedia, Benchmark and Extension.
His Computational problem research is multidisciplinary, incorporating elements of Evolvable hardware, Software and Carbon nanotube.
Between 2014 and 2020, his most popular works were:
- Cryptographic Boolean functions (36 citations)
- Introducing a cross platform open source Cartesian Genetic Programming library (29 citations)
- Evolving Carbon Nanotube Reservoir Computers (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Programming language
The scientist’s investigation covers issues in Genetic programming, Theoretical computer science, Boolean function, Computational problem and Evolutionary algorithm.
His is doing research in Cartesian genetic programming and Genetic representation, both of which are found in Genetic programming.
Julian F. Miller interconnects Local optimum and Identification in the investigation of issues within Genetic representation.
As part of the same scientific family, Julian F. Miller usually focuses on Boolean function, concentrating on Stream cipher and intersecting with Nonlinear element, Evolutionary computation, Nonlinear system and Algebraic number.
His research investigates the connection between Computational problem and topics such as Computation that intersect with problems in Task, Field-programmable gate array, Cellular automaton and Exploit.
He works mostly in the field of Artificial neural network, limiting it down to topics relating to Domain and, in certain cases, Artificial intelligence, as a part of the same area of interest.
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