The Emergence of Ontogenic Scaffolding in a Stochastic Development Environment

Evolutionary designs based upon Artificial Ontogenies are beginning to cross from virtual to real environments. In such systems the evolved genotype is an indirect, procedural representation of the final structure. To date, most Artificial Ontogenies have relied upon an error-free development process to generate their phenotypic structure. In this paper we explore the effects and consequences of developmental error on Artificial Ontogenies. In a simple evolutionary design task, and using an indirect procedural representation that lacks the ability to test intermediate results of development, we demonstrate the emergence of ontogenic mechanisms which are able to cope with developmental error.

[1]  Inman Harvey,et al.  Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics , 1995, ECAL.

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

[3]  Jordan B. Pollack,et al.  Evolution of complexity in real-world domains , 2001 .

[4]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[5]  Jordan B. Pollack,et al.  Three Generations of Automatically Designed Robots , 2001, Artificial Life.

[6]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[7]  Shigeyoshi Tsutsui,et al.  Advances in evolutionary computing: theory and applications , 2003 .

[8]  W. Langdon.  The evolution of size in variable length representations , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Gregory S. Hornby,et al.  Evolvable Systems for Space Applications , 2003 .

[10]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[11]  Gregory S. Hornby,et al.  Generative representations for evolutionary design automation , 2003 .

[12]  R. Pfeifer,et al.  Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny , 2001 .

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

[14]  Gregory S. Hornby,et al.  The advantages of generative grammatical encodings for physical design , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Carlos A. Coello Coello,et al.  An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[17]  Peter J. Bentley,et al.  Computational Embryology: Past, Present and Future , 2000 .

[18]  Marc Toussaint,et al.  Demonstrating the Evolution of Complex Genetic Representations: An Evolution of Artificial Plants , 2003, GECCO.

[19]  Gregory S. Hornby,et al.  Evolvable Hardware for Space Applications , 2004 .

[20]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[21]  Edwin D. de Jong,et al.  Reducing bloat and promoting diversity using multi-objective methods , 2001 .

[22]  Gregory Hornby,et al.  Generative Representations for Evolving Families of Designs , 2003, GECCO.