GP-DMD: a genetic programming variant with dynamic management of diversity

Nieto-Fuentes, Ricardo; Segura, Carlos

doi:10.1007/s10710-021-09426-4

Cited by 3 publications

(2 citation statements)

References 39 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of GP, several methods and strategies have been developed to measure diversity or maintain the level of population diversity [41,42]. Recently, Ricardo et al proposed a design concept for GP that relates the level of population diversity to the stopping criterion and the time elapsed to enhance the performance of GP in symbolic regression problems [43]. The most important feature of this design principle is the inclusion of a dynamic penalization scheme in the replacement phase, which aims to avoid the existence of similar individuals.…”

Section: A Novel Gp Methodsmentioning

confidence: 99%

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Processes

View full text Add to dashboard Cite

Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.

show abstract

Section: A Novel Gp Methodsmentioning

confidence: 99%

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Processes

View full text Add to dashboard Cite

show abstract

“…. Recently, Ricardo et al proposed a design concept for GP that relates the level of population diversity to the stopping criterion and the time elapsed to enhance the performance of GP in symbolic regression problems36 . The most important feature of this design principle is the inclusion of dynamic penalization scheme in the replacement phase, which aims to avoid the existence of similar individuals.…”

mentioning

confidence: 99%

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Sitahong

Yuan

et al. 2022

Preprint

View full text Add to dashboard Cite

Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is the first attempt at combining a novel GP method, GP with dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions.

show abstract

A replacement scheme based on dynamic penalization for controlling the diversity of the population in Genetic Programming

Nieto-Fuentes

Segura

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

GP-DMD: a genetic programming variant with dynamic management of diversity

Cited by 3 publications

References 39 publications

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

A replacement scheme based on dynamic penalization for controlling the diversity of the population in Genetic Programming

Contact Info

Product

Resources

About