In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Reinforcement Machine Learning System (RMLS) based on bio-inspired techniques. In this research an evolutionary mechanism based on Gene Expression Programming to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used too. The proposed approach was tested in an animat environment (artificial life) using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture.