Multiobjective techniques for the use of state in genetic programming applied to simulated car racing

Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful controllers in this problem domain.

[1]  Lee Spector,et al.  Cultural transmission of information in genetic programming , 1996 .

[2]  Astro Teller,et al.  The evolution of mental models , 1994 .

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  John R. Woodward.  Evolving Turing Complete representations , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[5]  John R. Koza,et al.  Genetic Programming II , 1992 .

[6]  David Andre,et al.  Evolution of mapmaking: learning, planning, and memory using genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  S. Luke,et al.  A Comparison of Crossover and Mutation in Genetic Programming , 1997 .

[8]  Julian Togelius,et al.  Evolving robust and specialized car racing skills , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  John R. Koza,et al.  Genetic Programming III: Darwinian Invention & Problem Solving , 1999 .

[10]  Wilker Shane Bruce.  Automatic generation of object-oriented programs using genetic programming , 1996 .

[11]  Julian Togelius,et al.  Evolving controllers for simulated car racing , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[13]  Evan Kirshenbaum,et al.  Genetic Programming with Statically Scoped Local Variables , 2000, GECCO.

[14]  Julian Togelius,et al.  Evolving controllers for simulated car racing using object oriented genetic programming , 2007, GECCO '07.

[15]  Julian Togelius,et al.  Arms Races and Car Races , 2006, PPSN.

[16]  Simon M. Lucas,et al.  Evolving a Statistics Class Using Object Oriented Evolutionary Programming , 2007, EuroGP.

[17]  Scott Brave,et al.  The evolution of memory and mental models using genetic programming , 1996 .

[18]  William B. Langdon,et al.  Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! , 1998 .