What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
Andries P. Engelbrecht spends much of his time researching Particle swarm optimization, Mathematical optimization, Artificial intelligence, Multi-swarm optimization and Cluster analysis.
The study incorporates disciplines such as Evolutionary computation, Swarm behaviour, Evolutionary algorithm and Nonlinear system in addition to Particle swarm optimization.
He has included themes like Convergence, Benchmark, Algorithm and Maxima and minima in his Mathematical optimization study.
The concepts of his Artificial intelligence study are interwoven with issues in Fitness landscape, Machine learning and Pattern recognition.
His Multi-swarm optimization research includes elements of Genetic algorithm, Fitness function, Metaheuristic and Fuzzy logic.
His work on Correlation clustering is typically connected to Color quantization as part of general Cluster analysis study, connecting several disciplines of science.
His most cited work include:
- Computational Intelligence: An Introduction (1792 citations)
- A Cooperative approach to particle swarm optimization (1672 citations)
- Fundamentals of Computational Swarm Intelligence (1475 citations)
What are the main themes of his work throughout his whole career to date?
Andries P. Engelbrecht spends much of his time researching Particle swarm optimization, Mathematical optimization, Artificial intelligence, Multi-swarm optimization and Algorithm.
Andries P. Engelbrecht combines subjects such as Algorithm design, Multi-objective optimization and Swarm behaviour with his study of Particle swarm optimization.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Convergence and Benchmark.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
As part of his studies on Multi-swarm optimization, Andries P. Engelbrecht often connects relevant areas like Crossover.
His Artificial neural network research is multidisciplinary, incorporating elements of Deep learning and Computational intelligence.
He most often published in these fields:
- Particle swarm optimization (50.00%)
- Mathematical optimization (46.45%)
- Artificial intelligence (33.65%)
What were the highlights of his more recent work (between 2016-2021)?
- Particle swarm optimization (50.00%)
- Mathematical optimization (46.45%)
- Algorithm (18.96%)
In recent papers he was focusing on the following fields of study:
Particle swarm optimization, Mathematical optimization, Algorithm, Optimization problem and Artificial intelligence are his primary areas of study.
His Multi-swarm optimization and Swarm intelligence study are his primary interests in Particle swarm optimization.
His work carried out in the field of Multi-swarm optimization brings together such families of science as Meta-optimization and Search algorithm.
His Mathematical optimization research incorporates themes from Stability, Convergence, Fitness landscape and Benchmark.
His Algorithm research integrates issues from Measure, Cluster analysis and Sensitivity.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
Between 2016 and 2021, his most popular works were:
- Computational Intelligence: An Introduction (1792 citations)
- Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications (103 citations)
- Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Particle swarm optimization, Mathematical optimization, Multi-swarm optimization, Artificial intelligence and Metaheuristic.
His biological study spans a wide range of topics, including Swarm behaviour, Curse of dimensionality and Benchmark.
In general Mathematical optimization study, his work on Swarm intelligence and Optimization problem often relates to the realm of Foundation, thereby connecting several areas of interest.
While the research belongs to areas of Swarm intelligence, Andries P. Engelbrecht spends his time largely on the problem of Evolutionary algorithm, intersecting his research to questions surrounding Multi-objective optimization, Cluster analysis and Genetic algorithm.
Many of his research projects under Multi-swarm optimization are closely connected to Distance ratio with Distance ratio, tying the diverse disciplines of science together.
His Artificial intelligence study incorporates themes from Machine learning and Generalization.
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