Graph-theoretic measure for active iGAs: interaction sizing and parallel evaluation ensemble
Since their inception, active interactive genetic algorithms have successfully combat user evaluation fatigue induced by repetitive evaluation. Their success originates on building models of the user preferences based on partial-order graphs to create a numeric synthetic fitness. Active interactive genetic algorithms can easily reduce up to seven times the number of evaluations required from the user by optimizing such a synthetic fitness. However, despite basic understanding of the underlying mechanisms there is still a lack of principled understanding of what properties make a partial ordering graph a successful model of user preferences. Also, there has been little research conducted about how to integrate together the contributions of different users to successfully capitalize on parallelized evaluation schemes. This paper addresses both issues describing: (1) what properties make a partial-order graph successful and accurate, and (2) how partial-order graphs obtained from different users can be merged meaningfully.