Design by Evolution: Advances in Evolutionary Design (Natural Computing Series)
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Evolution is Nature’s design process. The natural world is full of wonderful examples of its successes, from engineering design feats such as powered flight, to the design of complex optical systems such as the mammalian eye, to the merely stunningly beautiful designs of orchids or birds of paradise. With increasing computational power, we are now able to simulate this process with greater fidelity, combining complex simulations with high-performance evolutionary algorithms to tackle problems that used to be impractical.
This book showcases the state of the art in evolutionary algorithms for design. The chapters are organized by experts in the following fields: evolutionary design and "intelligent design" in biology, art, computational embryogeny, and engineering. The book will be of interest to researchers, practitioners and graduate students in natural computing, engineering design, biology and the creative arts.
Edinburgh in 2005 (CEC’05), and, partly as a response to the interesting ideas presented at that session, to embark on collecting together the contributions that make up this book. Along the way, we have realised that there are deep and beautiful connections between work done by scientists, engineers and artists, each with X Preface their own speciﬁc knowledge and culture, all striving to understand evolution as a design process or as a tool. We would like to thank all our authors, our
=-0.060 h 26,26 =1.099 g 29,26 =0.509 h 29,26 =-0.107 29 g 29,29 =-0.298 h 29,29 =1.254 h 28,28 =1.040 h 30,30 =1.047 absolute values of other gi,j ’s and h i,j ’s are less than 0.05 α 1 =0.975 α 11 =0.900 α 21 =0.870 β 1 =0.975 β 11 =0.899 β 21 =0.869 α 2 =0.836 α 12 =0.914 α 22 =0.731 β 2 =0.835 β 12 =0.913 β 22 =0.753 α 3 =0.912 α 13 =0.885 α 23 =1.006 β 3 =0.911 β 13 =0.885 β 23 =1.005 α 4 =0.936 α 14 =0.966 α 24 =0.977 β 4 =0.934 β 14 =0.966 β 24 =0.977 α 5 =0.945 α 15 =0.911 α 25
= just under 1, typically 0.99). The eﬀect of d at the beginning of the process is that genotypes that were randomly initialized with high ﬁtness values decrease as new genotypes rise to the top of the ﬁtness range as a result of positive user selection. The distribution of ﬁtness values begins to reﬂect user choice. As the initial random distribution of ﬁtness values gives way to meaningful values from user interaction, the decay operator then begins to serve a diﬀerent purpose: that of allowing
ensure the number of ‘[’ and ‘]’ symbols is equal. This can be achieved by adding or deleting bracket symbols as necessary. This is the preferred option. 8.4.2 Production Set Mutation In addition to individual symbol changes within successors, productions may be created and deleted. Here we assume a stochastic 0L-system, Gπ = V, P, ω, π as deﬁned in Sect. 8.2.2: (B.1) A stochastic production pn with associated probability π(pn ) may split into two stochastic productions, pn and pn∗ , i.e.: pn ⇒
J.: Aesthetic evolution of L-systems revisited. In: G. Raidl et al. (ed.) Applications of Evolutionary Computing (EvoWorkshops 2004). Lecture Notes in Computer Science 3005, pp. 477–488. Springer-Verlag, Berlin (2004) 13. McCormack, J.: Generative modelling with timed L-systems. In: J. Gero (ed.) Design Computing and Cognition ’04, pp. 157–175. Kluwer Academic Publishers, Dordrecht (2004) 14. McCormack, J.: Impossible nature: the art of Jon McCormack. Australian Centre for the Moving Image,