What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Mathematical optimization, Algorithm, Benchmark, Metaheuristic and Optimization problem.
Amir H. Gandomi has included themes like Chaotic, Krill and Nonlinear system in his Mathematical optimization study.
His work on Cuckoo search and Simulated annealing as part of general Algorithm study is frequently linked to Maxima and minima and Multi stage, therefore connecting diverse disciplines of science.
The Benchmark study combines topics in areas such as Regression, Bat algorithm and Constrained optimization.
His research integrates issues of Range and Firefly algorithm in his study of Metaheuristic.
His Optimization problem study combines topics from a wide range of disciplines, such as Harmony search and Local search.
His most cited work include:
- Beyond the hype (1671 citations)
- Salp Swarm Algorithm (1035 citations)
- Krill herd: A new bio-inspired optimization algorithm (1015 citations)
What are the main themes of his work throughout his whole career to date?
Amir H. Gandomi mainly investigates Mathematical optimization, Artificial intelligence, Genetic programming, Benchmark and Artificial neural network.
His research links Algorithm with Mathematical optimization.
His work carried out in the field of Artificial intelligence brings together such families of science as Big data, Machine learning, Data science and Pattern recognition.
His Genetic programming research incorporates elements of Simulated annealing, Structural engineering, Gene expression programming and Parametric statistics.
His Benchmark study combines topics in areas such as Differential evolution, Global optimization and Search algorithm.
His studies in Optimization problem integrate themes in fields like Genetic algorithm, Krill and Truss.
He most often published in these fields:
- Mathematical optimization (31.36%)
- Artificial intelligence (20.34%)
- Genetic programming (18.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (20.34%)
- Mathematical optimization (31.36%)
- Optimization problem (14.69%)
In recent papers he was focusing on the following fields of study:
Amir H. Gandomi mainly focuses on Artificial intelligence, Mathematical optimization, Optimization problem, Benchmark and Machine learning.
In general Artificial intelligence, his work in Deep learning, Artificial neural network and Support vector machine is often linked to Stock market linking many areas of study.
The concepts of his Mathematical optimization study are interwoven with issues in Uncertainty quantification and Sensitivity.
His Optimization problem study integrates concerns from other disciplines, such as Evolutionary algorithm, Particle swarm optimization and Metaheuristic.
Amir H. Gandomi focuses mostly in the field of Evolutionary algorithm, narrowing it down to topics relating to Genetic algorithm and, in certain cases, Algorithm.
As part of his studies on Benchmark, Amir H. Gandomi frequently links adjacent subjects like Search algorithm.
Between 2019 and 2021, his most popular works were:
- Marine Predators Algorithm: A nature-inspired metaheuristic (120 citations)
- Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies (71 citations)
- Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Optimization problem, Mathematical optimization, Artificial intelligence, Benchmark and Data mining.
His Optimization problem research is multidisciplinary, relying on both Particle swarm optimization, Soar, Differential evolution and MATLAB.
His Mathematical optimization research includes elements of Computational complexity theory and Sensitivity.
As a part of the same scientific family, Amir H. Gandomi mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Karush–Kuhn–Tucker conditions.
His Benchmark research is multidisciplinary, incorporating elements of Genetic algorithm, Metaheuristic and Search algorithm.
His biological study deals with issues like Stochastic optimization, which deal with fields such as Evolutionary computation.
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