Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Memory with memory: soft assignment in genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Building on success in genetic programming: adaptive variation and developmental evaluation
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
A linear estimation-of-distribution GP system
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Designing an Evolutionary Strategizing Machine for Game Playing and Beyond
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Unsupervised problem decomposition using genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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Biological organisms exhibit numerous types of plasticity, where they respond both developmentally and behaviorally to environmental factors. In some organisms, for example, environmental conditions can lead to the developmental expression of genes that would otherwise remain dormant, leading to significant phenotypic variation and allowing selection to act on these otherwise "invisible" genes. In contrast to biological plasticity, the vast majority of evolutionary computation systems, including genetic programming, are rigid and can only adapt to very limited external changes. In this paper we extend the N-gram GP system, a recently introduced estimation of distribution algorithm for program evolution, using Incremental Fitness-based Development (IFD), a novel technique which allows for developmental plasticity in the generation of linear-GP style programs. Tests with a large set of problems show that the new system outperforms the original N-gram GP system and is competitive with standard GP. Analysis of the evolved programs indicates that IFD allows for the generation of more complex programs than standard N-gram GP, with the generated programs often containing several separate sequences of instructions that are reused multiple times, often with variations.