Evolving Virtual Fireflies

In this paper, we present a study in the evolution of cooperative behavior, specifically synchronization, through digital evolution and multilevel selection. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instructionlevel mutations and natural selection. Multilevel selection links the survival of the individual to the survival of its group, thus encouraging cooperation. Previous approaches to designing synchronization algorithms have taken inspiration from nature: In the well-known firefly model, the only form of communication between agents is in the form of "flash" messages among neighbors. Here we demonstrate that populations of digital organisms, provided with a similar mechanism and minimal information about their environment, are capable of evolving algorithms for synchronization, and that the evolved behaviors are robust to message loss. Moreover, analysis of the dominant genome reveals that the evolved solution utilizes an adaptive frequency strategy strikingly similar to that observed in fireflies.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Walter Truszkowski,et al.  NASA's Swarm Missions: The Challenge of Building Autonomous Software , 2004, IT Prof..

[3]  Thomas D. Seeley,et al.  Honey Bee Colonies are Group‐Level Adaptive Units , 1997, The American Naturalist.

[4]  D. Floreano,et al.  Evolutionary Conditions for the Emergence of Communication in Robots , 2007, Current Biology.

[5]  Per Kristian Lehre,et al.  On the Effect of Populations in Evolutionary Multi-Objective Optimisation , 2006, Evolutionary Computation.

[6]  Christof Teuscher,et al.  On Fireflies, Cellular Systems, and Evolware , 2003, ICES.

[7]  D. Grimaldi,et al.  Evolution of the insects , 2005 .

[8]  Sanyou Zeng,et al.  Evolvable Systems: From Biology to Hardware, 7th International Conference, ICES 2007, Wuhan, China, September 21-23, 2007, Proceedings , 2007, ICES.

[9]  Jeffrey K. Conner,et al.  A Primer of Ecological Genetics , 2004 .

[10]  Charles Ofria,et al.  Evolution of an Adaptive Sleep Response in Digital Organisms , 2007, ECAL.

[11]  E. M. Eddy,et al.  The germ line and development. , 1996, Developmental genetics.

[12]  E. Southwick,et al.  Social synchronization of circadian rhythms of metabolism in honeybees (Apis mellifera) , 1987 .

[13]  Neville Passmore,et al.  Synchronized courtship in fiddler crabs , 1998, Nature.

[14]  Charles Ofria,et al.  Avida , 2004, Artificial Life.

[15]  M. Nowak.  Five Rules for the Evolution of Cooperation , 2006, Science.

[16]  Márk Jelasity,et al.  T-Man: Gossip-Based Overlay Topology Management , 2005, Engineering Self-Organising Systems.

[17]  B. Ermentrout,et al.  An adaptive model for synchrony in the firefly Pteroptyx malaccae , 1991 .

[18]  C. Adami,et al.  Evolution of Biological Complexity , 2000, Proc. Natl. Acad. Sci. USA.

[19]  G. O’Toole,et al.  Microbial Biofilms: from Ecology to Molecular Genetics , 2000, Microbiology and Molecular Biology Reviews.

[20]  David B. Knoester,et al.  Directed Evolution of Communication and Cooperation in Digital Organisms , 2007, ECAL.

[21]  Márk Jelasity,et al.  Firefly-inspired Heartbeat Synchronization in Overlay Networks , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[22]  Wolfgang Banzhaf,et al.  Advances in Artificial Life , 2003, Lecture Notes in Computer Science.

[23]  Robert T. Pennock,et al.  The evolutionary origin of complex features , 2003, Nature.

[24]  Roberto Montemanni,et al.  Design patterns from biology for distributed computing , 2006, TAAS.

[25]  Charles Ofria,et al.  Investigating the Emergence of Phenotypic Plasticity in Evolving Digital Organisms , 2007, ECAL.

[26]  R. Matthews,et al.  Ants. , 1898, Science.

[27]  Christoph Adami,et al.  Evolution of genetic organization in digital organisms , 1999, ArXiv.

[28]  J. Buck.  Synchronous Rhythmic Flashing of Fireflies. II. , 1938, The Quarterly Review of Biology.

[29]  John R. Koza,et al.  Genetic Programming IV: Routine Human-Competitive Machine Intelligence , 2003 .

[30]  Lee Spector,et al.  Emergence of Collective Behavior in Evolving Populations of Flying Agents , 2003, Genetic Programming and Evolvable Machines.

[31]  S. Ranji Ranjithan,et al.  Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation , 2005, GECCO '05.

[32]  Stefano Nolfi,et al.  Self-Organization of Communication in Evolving Robots , 2006 .

[33]  David B. Knoester,et al.  Cooperative network construction using digital germlines , 2008, GECCO '08.

[34]  Christian F. Tschudin,et al.  Experiments on the Automatic Evolution of Protocols Using Genetic Programming , 2005, WAC.

[35]  Jirí Jaros,et al.  An evolutionary approach to collective communication scheduling , 2007, GECCO '07.

[36]  Nancy A. Lynch,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[37]  Richard Anthony.  Emergence: a paradigm for robust and scalable distributed applications , 2004 .

[38]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .