What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Evolutionary computation, Evolutionary algorithm, Evolutionary programming, Mathematical optimization and Genetic representation.
Tournament selection is closely connected to Algorithm in his research, which is encompassed under the umbrella topic of Evolutionary computation.
His research investigates the connection between Evolutionary algorithm and topics such as Mutation that intersect with problems in Genetic search.
His studies examine the connections between Evolutionary programming and genetics, as well as such issues in Genetic programming, with regards to Classifier.
When carried out as part of a general Mathematical optimization research project, his work on Evolution strategy, Optimization problem and Knapsack problem is frequently linked to work in Simple and Quadratic function, therefore connecting diverse disciplines of study.
His research integrates issues of Theoretical computer science and Selection in his study of Genetic representation.
His most cited work include:
- Evolutionary algorithms in theory and practice (2375 citations)
- Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms (2250 citations)
- Handbook of Evolutionary Computation (2148 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Algorithm and Evolutionary computation.
The Mathematical optimization study which covers Benchmark that intersects with Set.
His study in Evolutionary algorithm is interdisciplinary in nature, drawing from both Genetic algorithm, Pareto principle, Mutation and Crossover.
His Artificial intelligence research includes elements of Machine learning and Task.
His Evolutionary computation research integrates issues from Theoretical computer science and Genetic programming.
His study in Human-based evolutionary computation, Java Evolutionary Computation Toolkit and Evolutionary music is carried out as part of his studies in Evolutionary programming.
He most often published in these fields:
- Mathematical optimization (38.70%)
- Evolutionary algorithm (24.29%)
- Artificial intelligence (20.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (38.70%)
- Artificial intelligence (20.62%)
- Machine learning (11.86%)
In recent papers he was focusing on the following fields of study:
Thomas Bäck focuses on Mathematical optimization, Artificial intelligence, Machine learning, Algorithm and Optimization problem.
His work focuses on many connections between Mathematical optimization and other disciplines, such as Benchmark, that overlap with his field of interest in Evolutionary computation, Set, Selection, Modular design and Particle swarm optimization.
His Evolutionary computation study combines topics from a wide range of disciplines, such as Dynamic problem and Black box.
His study looks at the relationship between Artificial intelligence and fields such as Task, as well as how they intersect with chemical problems.
Thomas Bäck has researched Optimization problem in several fields, including Genetic algorithm, CMA-ES and Heuristics.
Thomas Bäck conducts interdisciplinary study in the fields of Evolutionary algorithm and Point through his research.
Between 2017 and 2021, his most popular works were:
- IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics (25 citations)
- Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones (20 citations)
- Benchmarking discrete optimization heuristics with IOHprofiler (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Optimization problem, Benchmark, Algorithm and Mathematical optimization are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His Benchmark research includes themes of Evolutionary computation, Local search, Set and Modular design.
His Evolutionary computation research incorporates themes from Local optimum, Randomness, Mutation and Normal distribution.
Evolutionary algorithm and Global optimization are subfields of Mathematical optimization in which his conducts study.
Thomas Bäck merges Evolutionary algorithm with Performance indicator in his study.
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