Hoo, T.Ting, A.O'Neill, E.Allison, A.Abbott, D.Bergmann, N.W.2007-05-142007-05-142001Electronics and structures for MEMS II : 17-19 December, 2001, Adelaide, Australia / Neil W. Bergmann (ed.), pp. 380-39008194432120277-786Xhttp://hdl.handle.net/2440/28367© 2003 COPYRIGHT SPIE--The International Society for Optical EngineeringRedundancy is where multiple agents perform one task. On the other hand, pleiotropy is the inverse of redundancy- that is, where one agent multitasks. In real systems it is usual to find a mixture of both pleiotropic and redundant agents. In engineered systems we may see this in communication networks, computer systems, smart structures, nano-self-assembled systems etc. In biological systems, we can also observe the interplay of pleiotropy and redundancy from neural networks through to DNA coding. The open question is how to design a given complex system with the correct trade-off between redundancy and pleiotropy, in order to confer maximum robustness for lowest cost. Here we propose an evolutionary computational approach for exploring this trade-off in a toy model cellular automation, dubbed Real Life.enConference paper002001213910.1117/12.4491700001744635000412-s2.0-003576991261032Allison, A. [0000-0003-3865-511X]Abbott, D. [0000-0002-0945-2674]