Genetic transposition inspired incremental genetic programming for efficient coevolution of locomotion and sensing of simulated snake-like robot

Genetic transposition (GT) is a process of moving sequences of DNA to different positions within the genome of a single cell. It is recognized that the transposons (the jumping genes) facilitate the evolution of increasingly complex forms of life by providing the creative playground for the mutation where the latter could experiment with developing novel genetic structures without the risk of damaging the already existing, well-functioning genome. In this work we investigate the effect of a GT-inspired mechanism on the efficiency of genetic programming (GP) employed for coevolution of locomotion gaits and sensing of the simulated snake like robot (Snakebot). In the proposed approach, the task of coevolving the locomotion and the sensing morphology of Snakebot in a challenging environment is decomposed into two subtasks, implemented as two consecutive evolutionary stages. At first stage we employ GP to evolve a pool of simple, sensorless bots that are able to move fast in a smooth, open terrain. Then, during the second stage, we use these Snakebots to seed the initial population of the bots that are further subjected to coevolution of their locomotion control and sensing in a more challenging environment. For the second phase the seed is used as it is to create only part of a new individual, and the rest of the new individual’s genetic makeup is created by a mutant copy of the seed. Experimental results suggest that the proposed two-staged GT inspired incremental evolution contributes to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots.

[1]  Kim-Fung Man,et al.  A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[2]  J. McDonald,et al.  Copia is transcriptionally responsive to environmental stress. , 1985, Nucleic acids research.

[3]  Ivan Tanev,et al.  Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot , 2005, IEEE Transactions on Robotics.

[4]  John E. Perry,et al.  The effect of population enrichment in genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[5]  B. Uvarov,et al.  Grasshoppers and locusts. A handbook of general acridology. Volume 2. Behaviour, ecology, biogeography, population dynamics. , 1977 .

[6]  Leonard K. Kaczmarek,et al.  The Neuron: Cell and Molecular Biology , 1991 .

[7]  Julian Francis Miller,et al.  Towards the automatic design of more efficient digital circuits , 2000, Proceedings. The Second NASA/DoD Workshop on Evolvable Hardware.

[8]  Anabela Simões,et al.  Using Genetic Algorithms with Asexual Transposition , 2000, GECCO.

[9]  B. Mcclintock The origin and behavior of mutable loci in maize , 1950, Proceedings of the National Academy of Sciences.

[10]  Inman Harvey,et al.  Embracing Plagiarism: Theoretical, Biological and Empirical Justification for Copy Operators in Genetic Optimisation , 2005, Genetic Programming and Evolvable Machines.

[11]  G. Varley,et al.  Grasshoppers and Locusts , 1967 .

[12]  Ivan Tanev,et al.  Co-evolution of active sensing and locomotion gaits of simulated snake-like robot , 2008, GECCO '08.

[13]  Kevin Dowling,et al.  Limbless locomotion: learning to crawl , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[14]  山田 祐,et al.  Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発 , 2007 .

[15]  Juan Julián Merelo Guervós,et al.  Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem , 2009, ArXiv.

[16]  Anabela Simões,et al.  Transposition: A Biological-Inspired Mechanism to Use with Genetic Algorithms , 1999, ICANNGA.

[17]  L. Landweber,et al.  A Functional Role for Transposases in a Large Eukaryotic Genome , 2009, Science.

[18]  Ivan T. Tanev,et al.  DOM/XML-based portable genetic representation of the morphology, behavior and communication abilities of evolvable agents , 2004, Artificial Life and Robotics.

[19]  Stefano Nolfi,et al.  How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics , 1994 .

[20]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[21]  H. Morowitz The Emergence of Everything: How the World Became Complex , 2002 .

[22]  William B. Langdon,et al.  Seeding Genetic Programming Populations , 2000, EuroGP.

[23]  広瀬 茂男,et al.  Biologically inspired robots : snake-like locomotors and manipulators , 1993 .

[24]  Licheng Jiao,et al.  Gene transposon based clonal selection algorithm for clustering , 2009, GECCO '09.

[25]  Gary B. Fogel,et al.  A Clustal alignment improver using evolutionary algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[26]  John R. Koza,et al.  Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming , 2000, Genetic Programming and Evolvable Machines.