Effect of Crowd Composition on the Wisdom of Artificial Crowds Metaheuristic

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Springer International Publishing
This paper investigates the impact that task difficulty and crowd composition have on the success of the Wisdom of Artificial Crowds metaheuristic. The metaheuristic, which is inspired by the wisdom of crowds phenomenon, combines the intelligence from a group of optimization searches to form a new solution. Unfortunately, the aggregate formed by the metaheuristic is not always better than the best individual solution within the crowd, and little is known about the variables which maximize the metaheuristic’s success. Our study offers new insights into the influential factors of artificial crowds and the collective intelligence of multiple optimization searches performed on the same problem. The results show that favoring the opinions of experts (i.e., the better searches) improves the chances of the metaheuristic succeeding by more than 15% when compared to the traditional means of equal weighting. Furthermore, weighting expertise was found to require smaller crowd sizes for the metaheuristic to reach its pea...
Computer Science, Medicine, Artificial intelligence, Computer Security, Machine Learning, Radiology, Weighting, Metaheuristic
Lowrance, Christopher J., Dominic M. Larkin, and Sang M. Yim. “Effect of Crowd Composition on the Wisdom of Artificial Crowds Metaheuristic.” Lecture Notes in Computer Science, 2018, 539–51. https://doi.org/10.1007/978-3-030-04651-4_36.