Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Solving Master Mind Using GAs and Simulated Annealing: A Case of Dynamic Constraint Optimization
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
IEEE Transactions on Evolutionary Computation
Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Solving real-valued optimisation problems using cartesian genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
How functional dependency adapts to salience hierarchy in the GAuGE system
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Changing the genospace: solving GA problems with Cartesian genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Solving sudoku with the GAuGE system
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
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This paper describes the GAUGE system, Genetic Algorithms Using Grammatical Evolution. GAUGE is a position independent Genetic Algorithm that uses Grammatical Evolution with an attribute grammar to dictate what position a gene codes for. GAUGE suffers from neither under-specification nor over-specification, is guaranteed to produce syntactically correct individuals, and does not require any repair after the application of genetic operators.GAUGE is applied to the standard onemax problem, with results showing that its genotype to phenotype mapping and position independence nature do not affect its performance as a normal genetic algorithm. A new problem is also presented, a deceptive version of the Mastermind game, and we show that GAUGE possesses the position independence characteristics it claims, and outperforms several genetic algorithms, including the competent genetic algorithm messyGA.