Year: 2013
Type: preprint
Abstract: Evaluating a global optimal point in many global optimization problems in large space is required to more calculations. In this paper, there is presented a new approach for the continuous functions optimization with rotational mutation and crossover operator. This proposed method (RMC) starts from the point which has best fitness value by elitism mechanism and after that rotational mutation and crossover operator are used to reach optimal point. RMC method is implemented by GA (Briefly RMCGA) and is compared with other wellknown algorithms such as: DE, PGA, Grefensstette and Eshelman[15,16] and numerical and simulating results show that RMCGA achieve global optimal point with more decision by smaller generations.
Source: arXiv (Cornell University)
Author: Vali, Masoumeh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1642237635