Abstract
In this paper, we show that it is possible to use electroencephalography (EEG) and multi-brain computing with two humans to guide an Interactive Genetic Algorithm (IGA) system. We show that combining neural activity across two brains increases accuracy to guide evolutionary search more effectively. The IGA system involves a simple task of evolving a polygon shape to approximate the shape of a target polygon. Two candidates visually inspected the evolved polygons and mentally ranked them (independently from each other) from 1×10 based on their similarity to the target polygon. In parallel, the IGA system evaluated the fitness of evolved polygons using a standard fitness function. The IGA system was run for a few generations, before evolution was paused and EEG signals were collected from the two candidates. The collected EEG signals were used to train a regression model that received unseen EEG as input and mapped this into fitness values. The trained model was then used to guide the IGA solely by using the EEG signals. Off-line experimental results showed that it was possible to build better regression models that are trained using two EEG signals to capture participants evaluation of fitness. This paper demonstrates the possibility of a new domain of applications for interactive evolution where standard fitness calculations can be replaced with multiple EEG signals for guiding an optimisation process.