Variations of Genetic Algorithms
arXiv:1911.00490v12 citations
Originality Synthesis-oriented
AI Analysis
This work addresses a specific optimization problem for researchers in evolutionary computation, but it is incremental as it applies existing GA methods to a known benchmark.
The paper tackled optimizing the Schaffer F6 function by developing four variations of Genetic Algorithms, achieving the goal of solving it in fewer than 4000 function evaluations over 30 runs.
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.