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
A Hybrid Genetic Algorithm for the Maximum Clique Problem
Proceedings of the 6th International Conference on Genetic Algorithms
Proof verification and hardness of approximation problems
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Genetic Programming and Evolvable Machines
Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Computational Complexity, Genetic Programming, and Implications
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
A Puzzle to Challenge Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Collective adaptation: The exchange of coding segments
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
What makes a problem GP-hard? validating a hypothesis of structural causes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolutionary approaches to the generation of optimal error correcting codes
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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We have attempted to solve the Maximum Clique Problem using a simple genetic program. The program language consists of a Union operator and vertex numbers. Our results compare favorably with complex genetic algorithms. We hypothesize that our use of genetic programming is particularly effective at exploiting the mechanism embodied by the Building Block Hypothesis.