What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Algorithm
Janez Brest mainly focuses on Mathematical optimization, Algorithm, Evolutionary computation, Evolutionary algorithm and Differential evolution.
Mathematical optimization is represented through his Meta-optimization and Optimization problem research.
His work deals with themes such as Mutation and Benchmark, which intersect with Algorithm.
His work on IEEE Congress on Evolutionary Computation as part of general Benchmark study is frequently linked to Adaptive control, therefore connecting diverse disciplines of science.
In his study, Reduction and Algorithm design is inextricably linked to Set, which falls within the broad field of Evolutionary computation.
His Differential evolution research integrates issues from CMA-ES, Evolution strategy and Chaotic.
His most cited work include:
- Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2280 citations)
- A comprehensive review of firefly algorithms (676 citations)
- A Brief Review of Nature-Inspired Algorithms for Optimization (295 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Differential evolution, Algorithm, Artificial intelligence and Evolutionary algorithm.
His Mathematical optimization study integrates concerns from other disciplines, such as Set and Benchmark.
His study on Differential evolution algorithm is often connected to Mechanism as part of broader study in Differential evolution.
His Algorithm research is multidisciplinary, incorporating elements of Function and Solver.
His Artificial intelligence research also works with subjects such as
- Swarm intelligence which is related to area like Firefly algorithm and Bat algorithm,
- Speech recognition most often made with reference to Natural language processing.
His work carried out in the field of Evolutionary algorithm brings together such families of science as Continuous optimization, Local search and Metaheuristic.
He most often published in these fields:
- Mathematical optimization (38.93%)
- Differential evolution (36.64%)
- Algorithm (25.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Differential evolution (36.64%)
- Algorithm (25.95%)
- Optimization problem (12.98%)
In recent papers he was focusing on the following fields of study:
Differential evolution, Algorithm, Optimization problem, Benchmark and Artificial intelligence are his primary areas of study.
His Differential evolution study is associated with Mathematical optimization.
His study in Algorithm is interdisciplinary in nature, drawing from both Function, Upper and lower bounds, Interval and Memetics.
His Optimization problem research incorporates themes from Field, Global optimization and Scale.
The study incorporates disciplines such as Evolutionary computation and Optimization algorithm in addition to Benchmark.
His studies in Artificial intelligence integrate themes in fields like Natural language processing, Machine learning and Pattern recognition.
Between 2017 and 2021, his most popular works were:
- A review of the recent use of Differential Evolution for Large-Scale Global Optimization: An analysis of selected algorithms on the CEC 2013 LSGO benchmark suite (18 citations)
- Protein folding optimization using differential evolution extended with local search and component reinitialization (14 citations)
- Improved Differential Evolution for Large-Scale Black-Box Optimization (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
Janez Brest focuses on Differential evolution, Optimization problem, Evolutionary algorithm, Mathematical optimization and Benchmark.
His Differential evolution study deals with the bigger picture of Artificial intelligence.
His study with Optimization problem involves better knowledge in Algorithm.
His work on Global optimization as part of general Algorithm study is frequently connected to Suite, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The various areas that he examines in his Evolutionary algorithm study include Local optimum, Local search and Component.
His study in Evolutionary computation and Linear programming falls under the purview of Mathematical optimization.
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