On understanding types, data abstraction, and polymorphism
ACM Computing Surveys (CSUR) - The MIT Press scientific computation series
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving recursive programs for tree search
Advances in genetic programming
Type inheritance in strongly typed genetic programming
Advances in genetic programming
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
Proceedings of the 6th International Conference on Genetic Algorithms
Methods to Evolve Legal Phenotypes
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Strongly typed genetic programming
Evolutionary Computation
Scaling of program fitness spaces
Evolutionary Computation
Evolutionary unit testing of object-oriented software using strongly-typed genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
Proceedings of the 9th annual conference on Genetic and evolutionary computation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An improved representation for evolving programs
Genetic Programming and Evolvable Machines
An analytical inductive functional programming system that avoids unintended programs
PEPM '12 Proceedings of the ACM SIGPLAN 2012 workshop on Partial evaluation and program manipulation
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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Types have been introduced to Genetic Programming (GP) by researchers with different motivation. We present the concept of types in GP and introduce a typed GP system, PolyGP, that supports polymorphism through the use of three different kinds of type variable. We demonstrate the usefulness of this kind of polymorphism in GP by evolving two polymorphic programs (nth and map) using the system. Based on the analysis of a series of experimental results, we conclude that this implementation of polymorphism is effective in assisting GP evolutionary search to generate these two programs. PolyGP may enhance the applicability of GP to a new class of problems that are difficult for other polymorphic GP systems to solve.