Genetic algorithm, Artificial intelligence, Mathematical optimization, Genetic programming and Evolutionary algorithm are his primary areas of study. His Genetic algorithm research is multidisciplinary, incorporating elements of Artificial neural network, Algorithm and Crossover. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Machine learning, Data mining and Pattern recognition.
His Premature convergence study in the realm of Mathematical optimization interacts with subjects such as Robust design. He combines subjects such as Control engineering, Theoretical computer science, Bond graph and Robust control with his study of Genetic programming. His Evolutionary algorithm research incorporates themes from Evolutionary computation, Multi-objective optimization and Pareto principle.
His primary areas of investigation include Mathematical optimization, Genetic algorithm, Artificial intelligence, Evolutionary algorithm and Genetic programming. His studies link Benchmark with Mathematical optimization. The concepts of his Genetic algorithm study are interwoven with issues in Algorithm, Robustness and Crossover.
His biological study spans a wide range of topics, including Machine learning, Computer vision and Pattern recognition. His Evolutionary algorithm research includes elements of Pareto principle and Selection. His Genetic programming study combines topics in areas such as Computer-automated design, Theoretical computer science and Bond graph.
His primary areas of study are Mathematical optimization, Evolutionary algorithm, Multi-objective optimization, Optimization problem and Benchmark. His Mathematical optimization study incorporates themes from Computational intelligence and Constraint. He has researched Evolutionary algorithm in several fields, including Quality, Theoretical computer science, Linear model, Selection and Algorithm.
His Multi-objective optimization study combines topics from a wide range of disciplines, such as Sorting and Genetic algorithm. His research investigates the connection with Genetic algorithm and areas like Search algorithm which intersect with concerns in Data mining. Artificial intelligence covers Erik D. Goodman research in Benchmark.
Erik D. Goodman mainly focuses on Evolutionary algorithm, Mathematical optimization, Multi-objective optimization, Artificial intelligence and Optimization problem. His research integrates issues of Structure, Theoretical computer science, Position and Benchmark in his study of Evolutionary algorithm. The various areas that Erik D. Goodman examines in his Mathematical optimization study include Computational intelligence, Linear regression, Linear model, Support vector machine and Sorting.
Erik D. Goodman combines subjects such as Feature vector and Nonlinear system with his study of Multi-objective optimization. Artificial intelligence and Genetic algorithm are commonly linked in his work. His research integrates issues of Algorithm, Variable length and Curse of dimensionality in his study of Genetic algorithm.
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Dimensionality reduction using genetic algorithms
M.L. Raymer;W.F. Punch;E.D. Goodman;L.A. Kuhn.
IEEE Transactions on Evolutionary Computation (2000)
Further Research on Feature Selection and Classification Using Genetic Algorithms
William F. Punch;Erik D. Goodman;Min Pei;Lai Chia-Shun.
international conference on genetic algorithms (1993)
Coarse-grain parallel genetic algorithms: categorization and new approach
Shyh-Chang Lin;W.F. Punch;E.D. Goodman.
international parallel and distributed processing symposium (1994)
NSGA-Net: neural architecture search using multi-objective genetic algorithm
Zhichao Lu;Ian Whalen;Vishnu Boddeti;Yashesh Dhebar.
genetic and evolutionary computation conference (2019)
Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm.
Michael L. Raymer;Paul C. Sanschagrin;William F. Punch;Sridhar Venkataraman.
Journal of Molecular Biology (1997)
Method and product for determining salient features for use in information searching
William F. Punch;Marilyn R. Wulfekuhler;Erik D. Goodman.
A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem.
Shyh-Chang Lin;Erik D. Goodman;William F. Punch.
A Standard GA Approach to Native Protein Conformation Prediction
Arnold L. Patton;William F. Punch;Erik D. Goodman.
international conference on genetic algorithms (1995)
Direct dimensional NC verification
J. H. Oliver;E. D. Goodman.
Computer-aided Design (1990)
Push and pull search for solving constrained multi-objective optimization problems
Zhun Fan;Wenji Li;Xinye Cai;Hui Li.
Swarm and evolutionary computation (2019)
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