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
First draft of a report on the EDVAC
First draft of a report on the EDVAC
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
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Memory with Memory in Tree-Based Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Developmental plasticity in linear genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Soft memory for stock market analysis using linear and developmental genetic programming
Proceedings of the 11th 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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Based in part on observations about the incremental nature of most state changes in biological systems, we introduce the idea of Memory with Memory in Genetic Programming (GP), where we use "soft" assignments to registers instead of the "hard" assignments used in most computer science (including traditional GP). Instead of having the new value completely overwrite the old value of the register, these soft assignments combine the old and new values. We then report on extensive empirical tests (a total of 12,800 runs) on symbolic regression problems where Memory with Memory GP almost always does as well as traditional GP, while significantly outperforming it in several cases. Memory with Memory GP also tends to be far more consistent, having much less variation in its best-of-run fitnesses than traditional GP. The data suggest that Memory with Memory GP works by successively refining an approximate solution to the target problem. This means it can continue to improve (if slowly) over time, but that it is less likely to get the sort of exact solution that one might find with traditional GP. The use of soft assignment also means that Memory with Memory GP is much less likely to have truly ineffective code, but the action of successive refinement of approximations means that the average program size is often larger than with traditional GP.