Abstract
This paper presents a general method for assessment of the performance of a genetic algorithm (GA) in cases where the global optimum of the objective function is unknown. The method involves discretization of the search space, making it possible to apply a brute force calculation to find the global optimum for the discretized case. Then, this method is used to study the performance of a GA applied to the problem of speed profile optimization for heavy-duty vehicles, in which the optimization must be carried out within a rather short time. In this performance analysis, the discretization involves generating speed profiles as piecewise linear functions. It is demonstrated that the GA is able to find near-optimal solutions for the cases considered here: The speed profiles generated by the GA have objective function values that are typically within 2% of the global optimum.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
