Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Massively parallel genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Proceedings of the 6th International Conference on Genetic Algorithms
General Schema Theory for Genetic Programming with Subtree-Swapping Crossover
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
High-performance, parallel, stack-based genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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Sub-machine-code GP (SMCGP) is a technique to speed up genetic programming (GP) and to extend its scope based on the idea of exploiting the internal parallelism of sequential CPUs. In previous work [20] we have shown examples of applications of this technique to the evolution of parallel programs and to the parallel evaluation of 32 or 64 fitness cases per program execution in Boolean classification problems. After recalling the basic features of SMCGP, in this paper we first apply this technique to the problem of evolving parallel binary multipliers.s.Then we describe how SMCGP can be extended to process multiple fitness cases per program execution in continuous symbolic regression problems where inputs and outputs are real-valued numbers, reporting experimental results on a quartic polynomial approximation task.