Symbolic regression and evolutionary computation in setting an optimal trajectory for a robot

The paper deals with a novelty tool for symbolic regression - Analytic Programming (AP) which is able to solve various problems from the symbolic regression domain. One of tasks for it can be setting an optimal trajectory for artificial ant on Santa Fe trail which is the main application of Analytic Programming in this paper. In this contribution main principles of AP are described and explained. In second part of the article how AP was used for setting an optimal trajectory for artificial ant according the user requirements is in detail described. AP is a superstructure of evolutionary algorithms which are necessary to run AP. In this contribution 3 evolutionary algorithms were used - Self Organizing Migrating Algorithm, Differential Evolution and Simulated Annealing. The results show that the first two used algorithms were more successful than not so robust Simulated Annealing.

[1]  Conor Ryan,et al.  Grammatical evolution , 2007, GECCO '07.

[2]  N. Asokan,et al.  Designing a Generic Payment Service , 1998, IBM Syst. J..

[3]  José María Sierra,et al.  Payment in a Kiosk Centric Model with Mobile and Low Computational Power Devices , 2006, ICCSA.

[4]  Ivan Zelinka,et al.  Boolean symmetry function synthesis by means of arbitrary evolutionary algorithms - comparative study , 2004 .

[5]  Michael O'Neill,et al.  Grammatical evolution - evolutionary automatic programming in an arbitrary language , 2003, Genetic programming.

[6]  Hugo Krawczyk,et al.  Design, implementation, and deployment of the iKP secure electronic payment system , 2000, IEEE Journal on Selected Areas in Communications.

[7]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[8]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[9]  Bala Srinivasan,et al.  A secure account-based mobile payment protocol , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[10]  Zuzana Kominkova Oplatkova.  Optimal trajectory of robots using symbolic regres... , 2005 .

[11]  Ivan Zelinka,et al.  Mechanical engineering design optimization by differential evolution , 1999 .

[12]  Conor Ryan,et al.  An Investigation into the Use of Different Search Strategies with Grammatical Evolution , 2002, EuroGP.

[13]  Deren Chen,et al.  Generating digital signatures on mobile devices , 2004, 18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004..

[14]  Leandros Tassiulas,et al.  Security Issues in M-Commerce: A Usage-Based Taxonomy , 2001, E-Commerce Agents.

[15]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[16]  Ivan Zelinka,et al.  Investigation on artificial ant using analytic programming , 2006, GECCO '06.

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.