Fast Random Genetic Search for Large-Scale RTS Combat Scenarios

Abstract

This paper makes a contribution to the advancement of artificial intelligence in the context of multi-agent planning for large-scale combat scenarios in RTS games. This paper introduces Fast Random Genetic Search (FRGS), a genetic algorithm which is characterized by a small active population, a crossover technique which produces only one child, dynamic mutation rates, elitism, and restrictions on revisiting solutions. This paper demonstrates the effectiveness of FRGS against a static AI and a dynamic AI using the Portfolio Greedy Search (PGS) algorithm. In the context of the popular Real-Time Strategy (RTS) game, StarCraft, this paper shows the advantages of FRGS in combat scenarios up to the maximum size of 200 vs. 200 units under a 40 ms time constraint.