Competitive coevolution for defense and security: Elo-based similar-strength opponent sampling

Competitive coevolution is an important technique for fields such as defense and security which inherently involve adversarial games. One advantage that the field of computer security has in particular is that games in this space are often naturally able to be simulated at a high fidelity by interacting with the involved software or hardware directly. However, such high-fidelity evaluations are typically slow, so it is especially important in these cases to get as much useful information out of as few evaluations as possible. This paper proposes a new competitive coevolutionary evaluation method of Similar-Strength Opponent Sampling, which selects opponent pairings of similar skill levels so that evaluations can more efficiently distinguish the performances of similar individuals. This is enabled through the use of Elo ratings as a surrogate fitness function that prevents bias against individuals assigned stronger opponents. Care is taken to ensure that this technique is applicable to complex games where there is no explicit winner or loser, allowing ratings to be based on relative fitness. Mixed results are presented, showing that significant benefits are gained from pairing similar-strength opponents, but finding that the use of Elo rating instead of raw fitness harms evolution for intransitive games.