Optimization of Control Parameters for Genetic Algorithms

1986/01/01 by John Grefenstette, John J. Grefenstette · 2,862 citations
Engineering · Mathematics · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #Algorithm #Artificial intelligence #Class (philosophy) #Computer science #Control Systems and Identification #Engineering #Genetic algorithm #Machine learning #Mathematical optimization #Mathematics #Meta-optimization #Optimization algorithm #Optimization problem #Quality control and genetic algorithms #Set (abstract data type) #Task (project management) #Variety (cybernetics)

paper · doi:10.1109/tsmc.1986.289288

published in IEEE Transactions on Systems Man and Cybernetics 16(1), 122-128 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1986/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/20

Abstract

The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.

Citations

• An Automatic Method for Finding the Greatest or Least Value of a Function

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