Facilitating evolutionary innovation by developmental modularity and variability

Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of "evo-devo" computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.

[1]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[2]  Dan Braha,et al.  Complex Engineered Systems: Science Meets Technology , 2010 .

[3]  N. Suh,et al.  Complex Engineered Systems , 2006 .

[4]  Jordan B. Pollack,et al.  Automatic design and manufacture of robotic lifeforms , 2000, Nature.

[5]  L. Wolpert.  Positional information and the spatial pattern of cellular differentiation. , 1969, Journal of theoretical biology.

[6]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[7]  J. Miller,et al.  15 – Evolving the program for a cell: from French flags to Boolean circuits , 2003 .

[8]  Grzegorz Rozenberg,et al.  Cell division patterns: Syntactical description and implementation , 1982, Comput. Graph. Image Process..

[9]  Peter J. Bentley,et al.  Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem , 1999, GECCO.

[10]  Jordan B. Pollack,et al.  Modular Interdependency in Complex Dynamical Systems , 2005, Artificial Life.

[11]  M. Laubichler.  Review of: Carroll, Sean B., Jennifer K. Grenier and Scott D. Weatherbee: From DNA to diversity : molecular genetics and the evolution of animal design. Malden, Mass [u.a.]: Blackwell Science 2001 , 2003 .

[12]  Daniel Coore,et al.  Botanical computing: a developmental approach to generating interconnect topologies on an amorphous computer , 1999 .

[13]  René Doursat,et al.  Programmable Architectures That Are Complex and Self-Organized - From Morphogenesis to Engineering , 2008, ALIFE.

[14]  Jacob Beal,et al.  Infrastructure for engineered emergence on sensor/actuator networks , 2006, IEEE Intelligent Systems.

[15]  Mihaela Ulieru,et al.  Emergent engineering for the management of complex situations , 2008, Autonomics.

[16]  R. Doursat.  The Growing Canvas of Biological Development: Multiscale Pattern Generation on an Expanding Lattice of Gene Regulatory Nets , 2010 .

[17]  W. McCarthy.  Programmable matter , 2000, Nature.

[18]  Marco Dorigo,et al.  Morphology control in a multirobot system , 2007, IEEE Robotics & Automation Magazine.

[19]  Jordan B. Pollack,et al.  Creating High-Level Components with a Generative Representation for Body-Brain Evolution , 2002, Artificial Life.

[20]  Risto Miikkulainen,et al.  A Taxonomy for Artificial Embryogeny , 2003, Artificial Life.

[21]  Enrico Coen,et al.  The Art of Genes , 1999 .

[22]  Radhika Nagpal,et al.  Programmable self-assembly using biologically-inspired multiagent control , 2002, AAMAS '02.

[23]  René Doursat,et al.  Organically Grown Architectures: Creating Decentralized, Autonomous Systems by Embryomorphic Engineering , 2008, Organic Computing.

[24]  Dario Floreano,et al.  Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies , 2008 .