What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
Gabriela Ochoa mainly focuses on Artificial intelligence, Heuristics, Hyper-heuristic, Mathematical optimization and Machine learning.
Her biological study focuses on Heuristic.
Gabriela Ochoa combines subjects such as Local search and Search algorithm with her study of Heuristic.
Her study looks at the intersection of Heuristics and topics like Problem domain with Genetic program and Variable neighborhood search.
As a part of the same scientific family, Gabriela Ochoa mostly works in the field of Mathematical optimization, focusing on Fitness landscape and, on occasion, Graph theory, Combinatorics, Context and Feature.
She has included themes like Combinatorial search, Incremental heuristic search, Beam search and Iterated local search, Metaheuristic in her Machine learning study.
Her most cited work include:
- Hyper-heuristics: a survey of the state of the art (703 citations)
- A Classification of Hyper-heuristic Approaches (364 citations)
- Exploring Hyper-heuristic Methodologies with Genetic Programming (175 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Mathematical optimization, Fitness landscape, Artificial intelligence, Local optimum and Iterated local search.
Her biological study spans a wide range of topics, including Travelling salesman problem and Theoretical computer science.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Genetic algorithm, Machine learning and Heuristics.
Her studies deal with areas such as Beam search and Incremental heuristic search as well as Machine learning.
Her Heuristics research includes themes of Combinatorial search and Reinforcement learning.
Gabriela Ochoa interconnects Quadratic assignment problem, Global optimum, Network model and Complex network in the investigation of issues within Local optimum.
She most often published in these fields:
- Mathematical optimization (34.90%)
- Fitness landscape (32.81%)
- Artificial intelligence (29.17%)
What were the highlights of her more recent work (between 2018-2021)?
- Local optimum (27.60%)
- Fitness landscape (32.81%)
- Mathematical optimization (34.90%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Local optimum, Fitness landscape, Mathematical optimization, Local optima networks and Crossover.
Her Local optimum study incorporates themes from Iterated local search, Metaheuristic, Genetic algorithm and Theoretical computer science.
Her Iterated local search research focuses on subjects like Heuristics, which are linked to Heuristic and Algorithm.
Her work deals with themes such as Sampling, Context, Machine learning and Artificial intelligence, which intersect with Fitness landscape.
Gabriela Ochoa undertakes interdisciplinary study in the fields of Artificial intelligence and Neuroevolution through her works.
Her Heuristic study integrates concerns from other disciplines, such as Ranking and Selection.
Between 2018 and 2021, her most popular works were:
- Google Trends in Infodemiology and Infoveillance: Methodology Framework. (78 citations)
- A Classification of Hyper-Heuristic Approaches: Revisited (36 citations)
- Local optima networks for continuous fitness landscapes (6 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Gabriela Ochoa mainly investigates Fitness landscape, Local optimum, Crossover, Artificial intelligence and Machine learning.
Her Fitness landscape study combines topics in areas such as Ecology, Continuous optimization, Mathematical optimization and Extreme value theory.
Her research integrates issues of Particle swarm optimization, State, Differential evolution and Benchmark in her study of Local optimum.
The Crossover study combines topics in areas such as Travelling salesman problem, Partition and Applied mathematics.
Her work in the fields of Heuristic and Selection overlaps with other areas such as Hyper-heuristic, Development and Distance correlation.
Her Machine learning research is multidisciplinary, incorporating elements of Range and Representation.
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