Optimization of Control Parameters for Genetic Algorithms

1986/01/01 by John Grefenstette, John J. Grefenstette · 30 citations
Engineering · Mathematics · #Advanced Control Systems Optimization #Control Systems and Identification #Advanced Optimization Algorithms Research

paper · doi:10.1109/tsmc.1986.289288

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

Cited by

• Adaptive probabilities of crossover and mutation in genetic algorithms
• Informing AI Policy Assessment using Large-Scale Simulation of Interventions
• Time Sensitive Multiple POIs Route Planning on Bus Networks
• From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
• Hyper-heuristics: a survey of the state of the art
• Design and natural science research on information technology
• GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain\n Systems Modeled with Bayesian Belief Networks
• Learning to Extract Keyphrases from Text
• Qualities, challenges and future of genetic algorithms: a literature review
• Parameter Sensitivity Analysis of Social Spider Algorithm
• Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics
• A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation. [europepmc]
• Symmetry Based Automatic Evolution of Clusters: A New Approach to Data Clustering. [europepmc]
• Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem. [europepmc]
• An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes. [europepmc]
• Genetic Optimization Algorithm for Metabolic Engineering Revisited. [europepmc]
• Implementing Green Supply Chain Management for Online Pharmacies through a VADD Inventory Model. [europepmc]
• Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision. [europepmc]
• Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm. [europepmc]
• Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem. [europepmc]
• An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications. [europepmc]
• Hybrid-Flash Butterfly Optimization Algorithm with Logistic Mapping for Solving the Engineering Constrained Optimization Problems. [europepmc]
• CLSQL: Improved Q-Learning Algorithm Based on Continuous Local Search Policy for Mobile Robot Path Planning. [europepmc]
• Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis. [europepmc]
• Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach. [europepmc]
• CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures. [europepmc]
• Optimal design of graphene-based plasmonic enhanced photodetector using PSO. [europepmc]
• Optimal Design of Formulas for a Single Degree of Freedom Tuned Mass Damper Parameter Using a Genetic Algorithm and H 2 Norm. [europepmc]
• Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach. [europepmc]
• An advanced direct torque control for doubly fed induction motor using evolutionary computational techniques. [europepmc]

Related

• Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization
• Genetic algorithms: a survey
• Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms
• Genetic optimization algorithms applied toward mission computability models
• Genetic algorithm formulation and tuning with use of test functions