@inproceedings{ramirez2020tea,

title = {T-EA: A traceable evolutionary algorithm},

author = {Cristian Ramirez-Atencia and Tobias Benecke and Sanaz Mostaghim},

doi = {10.1109/CEC48606.2020.9185615},

isbn = {978-1-7281-6929-3},

year  = {2020},

date = {2020-09-03},

urldate = {2020-09-03},

booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1--8},

organization = {IEEE},

abstract = {In this paper, the influence of the initial population into successive generations in Evolutionary Algorithms (EAs) is studied as a problem-independent approach. For this purpose, the Traceable Evolutionary Algorithm (T-EA) is proposed. This algorithm keeps track of the influence of the individuals from the initial population over the generations of the algorithm. The algorithm has been implemented for both bit-string and integer vector representations. In addition, in order to study the general influence of each individual, new impact factor metrics have been proposed. In this way, we aim to provide tools to measure the influence of initial individuals on the final solutions. As a proof of concept, three classical optimization problems (One Max, 0/1 Knapsack and Unbounded Knapsack problems) are used. We provide a framework that allows to explain why some individuals in the initial population work better than others in relation with the corresponding fitness values.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}