Variations of Genetic Algorithms
Alison Jenkins, Vinika Gupta, Alexis Myrick, Mary Lenoir
TL;DR
This paper explores different variations of genetic algorithms to efficiently optimize the Schaffer F6 function, aiming to reduce the number of function evaluations needed.
Contribution
It introduces and compares four types of genetic algorithms, providing insights into their performance on a specific optimization problem.
Findings
All algorithms aimed to solve the Schaffer F6 function within 4000 evaluations.
Comparison of GGA, SSGA, SGGA, and (mu+mu)-GA on efficiency and effectiveness.
Potential improvements in genetic algorithm design for function optimization.
Abstract
The goal of this project is to develop the Genetic Algorithms (GA) for solving the Schaffer F6 function in fewer than 4000 function evaluations on a total of 30 runs. Four types of Genetic Algorithms (GA) are presented - Generational GA (GGA), Steady-State (mu+1)-GA (SSGA), Steady-Generational (mu,mu)-GA (SGGA), and (mu+mu)-GA.
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Taxonomy
TopicsNumerical Methods and Algorithms · Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms
MethodsGenetic Algorithms