What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Joshua Knowles mainly investigates Multi-objective optimization, Mathematical optimization, Evolutionary algorithm, Evolutionary computation and Local search.
His studies in Multi-objective optimization integrate themes in fields like Genetic algorithm, Optimization problem, Global optimization and Artificial intelligence.
His Mathematical optimization research is multidisciplinary, relying on both Algorithm, Covering problems and Selection.
His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Correlation clustering, Data mining and Conceptual clustering.
Joshua Knowles has included themes like Memetic algorithm, Cardinality and Heuristic in his Evolutionary computation study.
His Local search research incorporates elements of Graph, Evolutionary programming and Evolution strategy.
His most cited work include:
- Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy (1878 citations)
- The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation (1098 citations)
- The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation (736 citations)
What are the main themes of his work throughout his whole career to date?
Joshua Knowles mainly focuses on Mathematical optimization, Multi-objective optimization, Artificial intelligence, Evolutionary algorithm and Machine learning.
His work is connected to Optimization problem, Local search, Evolution strategy, Genetic algorithm and Combinatorial optimization, as a part of Mathematical optimization.
His Multi-objective optimization research is multidisciplinary, incorporating perspectives in Management science, Global optimization, Pareto principle, Selection and Ranking.
The Artificial intelligence study combines topics in areas such as Data mining and Pattern recognition.
His work carried out in the field of Evolutionary algorithm brings together such families of science as Evolutionary computation and Algorithm.
His Evolutionary computation study combines topics in areas such as Theoretical computer science and Constrained optimization.
He most often published in these fields:
- Mathematical optimization (34.97%)
- Multi-objective optimization (30.05%)
- Artificial intelligence (25.68%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (25.68%)
- Machine learning (16.39%)
- Microeconomics (3.83%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Microeconomics, Mathematical optimization and Multi-objective optimization.
In general Artificial intelligence, his work in Cluster analysis, Data set and Evolutionary algorithm is often linked to Protein structure prediction and Context linking many areas of study.
In Machine learning, Joshua Knowles works on issues like Identification, which are connected to Pattern recognition.
Evolutionary computation and Optimization problem are among the areas of Mathematical optimization where Joshua Knowles concentrates his study.
His research in Evolutionary computation focuses on subjects like Travelling salesman problem, which are connected to Local search.
His Multi-objective optimization research incorporates themes from Multiple-criteria decision analysis and Management science.
Between 2013 and 2021, his most popular works were:
- Molecular phenotyping of a UK population: defining the human serum metabolome. (148 citations)
- Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach (113 citations)
- Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach (113 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Machine learning, Artificial intelligence, Mathematical optimization, Decision tree and Optimization problem.
His work deals with themes such as Class, False positive rate and Identification, which intersect with Machine learning.
His work in the fields of Artificial intelligence, such as Data set and Ranking, overlaps with other areas such as Protein tertiary structure and Protein structure prediction.
His research in Multi-objective optimization, Travelling salesman problem and Evolutionary computation are components of Mathematical optimization.
The various areas that he examines in his Decision tree study include Learning classifier system and Tree.
Many of his studies involve connections with topics such as Multiple-criteria decision analysis and Optimization problem.
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