TY - JOUR
T1 - GRAPE
T2 - Grammatical Algorithms in Python for Evolution
AU - de Lima, Allan
AU - Carvalho, Samuel
AU - Dias, Douglas Mota
AU - Naredo, Enrique
AU - Sullivan, Joseph P.
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/9
Y1 - 2022/9
N2 - GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.
AB - GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.
KW - Grammatical Evolution
KW - Python
KW - tool
UR - http://www.scopus.com/inward/record.url?scp=85180820497&partnerID=8YFLogxK
U2 - 10.3390/signals3030039
DO - 10.3390/signals3030039
M3 - Article
AN - SCOPUS:85180820497
SN - 2624-6120
VL - 3
SP - 642
EP - 663
JO - Signals
JF - Signals
IS - 3
ER -