Towards evolving fault tolerant biologically inspired hardware using evolutionary algorithms

Embryonic hardware systems satisfy the fundamental characteristics found in nature which contribute to the development of any multi-cellular living being. Attempts of researchers´ in this field to learn from nature have yielded promising results; they proved the feasibility of applying nature-like mechanisms to the world of digital electronics with self-diagnostic and self-healing characteristics, Design by humans however often results in very complex hardware architectures, requiring a large amount of manpower and computational resources. A wider objective is to find novel solutions to design such complex architectures for Embryonic Systems, by problem decomposition and unique design methodologies so that system functionality and performance will not be compromised. Design automation using reconfigurable hardware and EA (evolutionary algorithm), such as GA (genetic algorithms), is one way to tackle this issue. This concept applies the notion of EHW (evolvable hardware) to the problem domain. Unlocking the power of EHW for both novel design solutions and for circuit optimisation has attracted many researchers since the early ´90s. The promise of using genetic algorithms through evolvable hardware design will, in this paper, be demonstrated by the authors by evolving a relatively simple combinatorial logic circuit (full-adder).