  Inspired by the notion of surprise for unconventional discovery we introduce
a general search algorithm we name surprise search as a new method of
evolutionary divergent search. Surprise search is grounded in the divergent
search paradigm and is fabricated within the principles of evolutionary search.
The algorithm mimics the self-surprise cognitive process and equips
evolutionary search with the ability to seek for solutions that deviate from
the algorithm's expected behaviour. The predictive model of expected solutions
is based on historical trails of where the search has been and local
information about the search space. Surprise search is tested extensively in a
robot maze navigation task: experiments are held in four authored deceptive
mazes and in 60 generated mazes and compared against objective-based
evolutionary search and novelty search. The key findings of this study reveal
that surprise search is advantageous compared to the other two search
processes. In particular, it outperforms objective search and it is as
efficient as novelty search in all tasks examined. Most importantly, surprise
search is faster, on average, and more robust in solving the navigation problem
compared to any other algorithm examined. Finally, our analysis reveals that
surprise search explores the behavioural space more extensively and yields
higher population diversity compared to novelty search. What distinguishes
surprise search from other forms of divergent search, such as the search for
novelty, is its ability to diverge not from earlier and seen solutions but
rather from predicted and unseen points in the domain considered.
