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Charles V. Camp
Researcher at University of Memphis
Publications - 67
Citations - 2640
Charles V. Camp is an academic researcher from University of Memphis. The author has contributed to research in topics: Genetic algorithm & Discrete optimization. The author has an hindex of 20, co-authored 67 publications receiving 2202 citations. Previous affiliations of Charles V. Camp include Middle East Technical University & University of Illinois at Urbana–Champaign.
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Journal ArticleDOI
Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm
TL;DR: A design procedure incorporating a simple genetic algorithm (GA) is developed for discrete optimization of two-dimensional structures.
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Design of space trusses using big bang–big crunch optimization
TL;DR: In this article, a procedure for designing low-weight space trusses based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for both discrete and continuous variable optimization.
Journal ArticleDOI
Design of Space Trusses Using Ant Colony Optimization
Charles V. Camp,Barron J. Bichon +1 more
TL;DR: In this paper, a design procedure utilizing an ant colony optimization (ACO) technique is developed for discrete optimization of space trusses, where the objective function considered is the total weight (or cost) of the structure subjected to material and performance constraints in the form of stress and deflection limits.
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Design of Nonlinear Framed Structures Using Genetic Optimization
TL;DR: This paper employs a group selection mechanism, discusses an improved adapting crossover operator, and provides recommendations on the penalty function selection, and compares the differences between optimized designs obtained by linear and geometrically nonlinear analyses.
Journal ArticleDOI
Design of Steel Frames Using Ant Colony Optimization
Charles V. Camp,Charles V. Camp,Barron J. Bichon,Barron J. Bichon,Scott P. Stovall,Scott P. Stovall +5 more
TL;DR: A design procedure utilizing an ant colony optimization (ACO) technique is developed for discrete optimization of steel frames and a comparison is presented between the ACO frame designs and designs developed using a genetic algorithm and classical continuous optimization methods.