What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Programming language
His main research concerns Artificial intelligence, Quality control and genetic algorithms, Genetic representation, Algorithm and Genetic algorithm.
His work on Local optimum as part of general Artificial intelligence research is often related to Baldwin effect, thus linking different fields of science.
L. Darrell Whitley interconnects Population-based incremental learning and Cultural algorithm in the investigation of issues within Quality control and genetic algorithms.
The concepts of his Genetic representation study are interwoven with issues in Parallel algorithm, Optimization problem and Gray code.
His Algorithm research includes themes of Computer program, Mathematical optimization and Crossover.
His Genetic algorithm research is multidisciplinary, relying on both Set, Executable and Operator.
His most cited work include:
- The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best (949 citations)
- Genetic algorithms and neural networks: optimizing connections and connectivity (615 citations)
- Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator (427 citations)
What are the main themes of his work throughout his whole career to date?
L. Darrell Whitley spends much of his time researching Mathematical optimization, Local search, Algorithm, Genetic algorithm and Artificial intelligence.
His Local optimum, Tabu search, Guided Local Search and Beam search study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Job shop scheduling, bridging the gap between disciplines.
His Local search study integrates concerns from other disciplines, such as Evolutionary algorithm, Maximum satisfiability problem and Crossover.
In general Algorithm, his work in Search algorithm and Gray code is often linked to Flow shop scheduling linking many areas of study.
He works mostly in the field of Genetic algorithm, limiting it down to topics relating to Travelling salesman problem and, in certain cases, Fitness landscape and Genetic operator, as a part of the same area of interest.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Hyperplane.
He most often published in these fields:
- Mathematical optimization (34.48%)
- Local search (24.14%)
- Algorithm (23.28%)
What were the highlights of his more recent work (between 2011-2020)?
- Local search (24.14%)
- Mathematical optimization (34.48%)
- Crossover (8.62%)
In recent papers he was focusing on the following fields of study:
L. Darrell Whitley focuses on Local search, Mathematical optimization, Crossover, Evolutionary algorithm and Travelling salesman problem.
His Local search research is multidisciplinary, incorporating perspectives in Discrete mathematics, Connected component, Simple and Theoretical computer science.
His Mathematical optimization study frequently draws connections between adjacent fields such as Algorithm.
His Time complexity study in the realm of Algorithm connects with subjects such as Echo.
His work deals with themes such as Genetic algorithm, Fitness landscape, Partition and Maximum satisfiability problem, which intersect with Crossover.
L. Darrell Whitley has researched Evolutionary algorithm in several fields, including Probability distribution, Polynomial, Combinatorial optimization and Mutation.
Between 2011 and 2020, his most popular works were:
- Genetic Algorithms - A Survey of Models and Methods. (29 citations)
- Gray box optimization for mk landscapes nk landscapes and max-ksat (21 citations)
- Computing the moments of k-bounded pseudo-Boolean functions over Hamming spheres of arbitrary radius in polynomial time (21 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Programming language
- Artificial intelligence
The scientist’s investigation covers issues in Local search, Crossover, Travelling salesman problem, Mathematical optimization and Mutation.
His Crossover study deals with Heuristic intersecting with Genetic algorithm and Simple.
L. Darrell Whitley is interested in Local optimum, which is a field of Mathematical optimization.
His biological study deals with issues like Gray box testing, which deal with fields such as Algorithm.
His research investigates the connection between Mutation and topics such as Probability distribution that intersect with problems in Evolutionary algorithm and Combinatorics.
His research integrates issues of Fitness landscape, Representation and Combinatorial optimization in his study of Evolutionary algorithm.
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