TY - GEN
T1 - Evolutionary Computing based Analysis of Diversity in Grammatical Evolution
AU - Youssef, Ayman
AU - Gupt, Krishn Kumar
AU - Raja, Muhammad Adil
AU - Murphy, Aidan
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/25
Y1 - 2021/3/25
N2 - Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple generations.In this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population.
AB - Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other. Subsequently, they fail to produce a variety of offspring. Grammatical Evolution (GE), being an Evolutionary Algorithm (EA), is also an aspirant of diversity. It allows a GE system to maintain a dynamic population over multiple generations.In this paper, we present our reflections about diversity estimates in a (large) number of experiments. We performed evolutionary experiments to estimate a bunch of well-known benchmark polynomials. We also employed hybrid optimization in our experiments. Our results are insightful. In this paper, we also test the effect of hybrid optimization algorithms integrated with GE on the diversity of the population.
KW - Diversity
KW - Evolutionary computing
KW - Grammatical evolution
KW - Hybrid optimization
KW - Symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=85104996215&partnerID=8YFLogxK
U2 - 10.1109/ICAIS50930.2021.9395792
DO - 10.1109/ICAIS50930.2021.9395792
M3 - Conference contribution
AN - SCOPUS:85104996215
T3 - Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021
SP - 1688
EP - 1693
BT - Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021
Y2 - 25 March 2021 through 27 March 2021
ER -