Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Linear Genetic Programming (Genetic and Evolutionary Computation)
Linear Genetic Programming (Genetic and Evolutionary Computation)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic programming for finite algebras
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolution of a local boundary detector for natural images via genetic programming and texture cues
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Parallel linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Evolving while-loop structures in genetic programming for factorial and ant problems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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In conventional Genetic Programming (GP), n programs are simultaneously evaluated and only the best programs will survive from one generation to the next. It is a pity as some programs might contain useful code that might be hidden or not evaluated due to the presence of introns. For example in regression, 0× (perfect code) will unfortunately not be assigned a good fitness and this program might be discarded due to the evolutionary process. In this paper, we develop a new form of GP called PhenoGP (PGP). PGP individuals consist of ordered lists of programs to be executed in which the ultimate goal is to find the best order from simple building-blocks programs. If the fitness remains stalled during the run, new building-blocks programs are generated. PGP seems to compare fairly well with canonical GP.