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 mental models
Advances in genetic programming
Evolving recursive programs for tree search
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Ant Colony Optimization
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Function choice, resiliency and growth in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cultural transmission of information in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Automatic generation of object-oriented programs using genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Novel loop structures and the evolution of mathematical algorithms
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
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We introduce Memory with Memory Genetic Programming (MwM-GP), where we use soft assignments and soft return operations. Instead of having the new value completely overwrite the old value of registers or memory, soft assignments combine such values. Similarly, in soft return operations the value of a function node is a blend between the result of a calculation and previously returned results. In extensive empirical tests, MwM-GP almost always does as well as traditional GP, while significantly outperforming it in several cases. MwM-GP also tends to be far more consistent than traditional GP. The data suggest that MwM-GP works by successively refining an approximate solution to the target problem and that it is much less likely to have truly ineffective code. MwM-GP can continue to improve over time, but it is less likely to get the sort of exact solution that one might find with traditional GP.