A. V. Kononova, F. Caraffini, H. Wang and T. Bäck, "Can Single Solution Optimisation Methods Be Structurally Biased?," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-9, doi: 10.1109/CEC48606.2020.9185494.
A. V. Kononova, F. Caraffini, H. Wang and T. Bäck, "Can Single Solution Optimisation Methods Be Structurally Biased?," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-9, doi: 10.1109/CEC48606.2020.9185494.
A. V. Kononova, F. Caraffini, H. Wang and T. Bäck, "Can Single Solution Optimisation Methods Be Structurally Biased?," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-9, doi: 10.1109/CEC48606.2020.9185494.
A. V. Kononova, F. Caraffini, H. Wang and T. Bäck, "Can Single Solution Optimisation Methods Be Structurally Biased?," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-9, doi: 10.1109/CEC48606.2020.9185494.
Abstract
This paper investigates whether optimisation methods with the population made up of one solution can suffer from structural bias just like their multisolution variants. Following recent results highlighting the importance of choice of strategy for handling solutions generated outside the domain, a selection of single solution methods are considered in conjunction with several such strategies. Obtained results are tested for the presence of structural bias by means of a traditional approach from literature and a newly proposed here statistical approach. These two tests are demonstrated to be not fully consistent. All tested methods are found to be structurally biased with at least one of the tested strategies. Confirming results for multisolution methods, it is such strategy that is shown to control the emergence of structural bias in single solution methods. Some of the tested methods exhibit a kind of structural bias that has not been observed before.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
