Abstract

This paper proposes a framework for the optimization of adversarial potential-based prey-predator-like problems. The adversarial potentials are decomposed onto a basis set with different weights. Each weight is individually optimized in a Particle Swarm Optimization-like manner using a Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The proposed results are illustrated via a "Cops & Robbers" scenario.