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12. 13. 14. 15. 16. 17. 18. 19. M. H. S. : Generic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications. : Promises and Challenges of Evolvable Hardware. : An Extrinsic Function-Level Evolvable Hardware Approach. : Principles in the Evolutionary Design of Digital Circuits – Part I. : Use of Particle Swarm Optimization to Design Combinational Logic Circuits. : Evolving Multiplier Circuits by Training Set and Training Vector Partitioning. : Two Self-Adaptive Crossover Operators for Genetic Programming.

O. (1977) 17. Makanin, G. : Equations in a free group. Izv. Akad. Nauk SSSR Ser. Mat. 46 (1982) 1199–1273 (in Russian; translation: Math. USSR Izv. 21 (1983)) 18. : Some decision problems for traces. Lect. Notes Comp. Sci. 1234 (1997) 248–257 19. Miasnikov, A. : Genetic algorithms and the Andrews-Curtis conjecture. Internat. J. Algebra Comput. 9 no. 6 (1999) 671–686 20. Miasnikov, A. , Myasnikov, A. : Whitehead method and genetic algorithms. Contemp. Math. (to appear) 21. Myasnikov, A. , Remeslennikov, V.

1 Introduction Genetic Programming (GP) [1] has been widely applied to different application areas. There are two main streams in GP, standard GP [2] and Linear structure GP (LGP) [3]. In standard GP, a genetic program is represented in a tree structure. g. crossovers and mutations) manipulate branches and leave nodes of program trees. LGP is based on the principle of register machines. In LGP, a genetic program is represented in a linear list of machine code instructions or high-level language statements.

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