Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of size and shape
Advances in genetic programming
Parallel distributed genetic programming
New ideas in optimization
Proceedings of the European Conference on Genetic Programming
Linear-Tree GP and Its Comparison with Other GP Structures
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Linear-Graph GP - A New GP Structure
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
Code growth in genetic programming
Code growth in genetic programming
Parsing and translation of expressions by genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Dormant program nodes and the efficiency of genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A new crossover technique for Cartesian genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Graph structured program evolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Linear Genetic Programming
A new, node-focused model for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Single Node Genetic Programming (SNGP) offers a new approach to GP in which every member of the population consists of just a single program node. Operands are formed from other members of the population, and evolution is driven by a hill-climbing approach using a single reversible operator. When the functions being used in the problem are free from side effects, it is possible to make use of a form of dynamic programming, which provides huge efficiency gains. In this research we turn our attention to the use of SNGP when the solution of problems relies on the presence of side effects. We demonstrate that SNGP can still be superior to conventional GP, and examine the role of evolutionary strategies in achieving this.