Recently, we proposed a neuromorphic intrinsic online evolvable hardware (EH) system designed to learn control laws of physical devices. Since we intend to eventually build this device using mixed signal VLSI techniques, and because we intend to address control applications in which small size and low power consumption are critical, we are extremely concerned with the design of physically compact devices. This paper focuses on the evolutionary algorithm (EA) portion of our proposed system. We discuss modifications to our previously reported *CGA that significantly increases its performance against dynamic optimization problems without significantly increasing the amount of hardware required for implementation. We demonstrate the efficacy of our improvement by testing against two series of moving peak benchmarks. We conclude with discussions of both the implications of our findings and our plans for future work.