Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Programming Perl (2nd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Constructing Correct Software: The Basics
Constructing Correct Software: The Basics
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Introduction to Formal Language Theory
Introduction to Formal Language Theory
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Programming for Pedestrians
Proceedings of the 5th International Conference on Genetic Algorithms
A Representation Scheme To Perform Program Induction in a Canonical Genetic Algorithm
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Evolving Chess Playing Programs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Polymorphism and Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Adaption of Operator Probabilities in Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Linear-Tree GP and Its Comparison with Other GP Structures
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Linear-Graph GP - A New GP Structure
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
Strongly typed genetic programming
Evolutionary Computation
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The benefits of computing with introns
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolving efficient list search algorithms
EA'09 Proceedings of the 9th international conference on Artificial evolution
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
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A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time.