Two self-adaptive crossover operators for genetic programming
Advances in genetic programming
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Exploring extended particle swarms: a genetic programming approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines
IEEE Transactions on Evolutionary Computation
Automatic design of ant algorithms with grammatical evolution
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
The importance of the learning conditions in hyper-heuristics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolving black-box search algorithms employing genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Towards a method for automatically evolving bayesian network classifiers
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper proposes a Grammatical Evolution framework to the automatic design of Evolutionary Algorithms. We define a grammar that has the ability to combine components regularly appearing in existing evolutionary algorithms, aiming to achieve novel and fully functional optimization methods. The problem of the Royal Road Functions is used to assess the capacity of the framework to evolve algorithms. Results show that the computational system is able to evolve simple evolutionary algorithms that can effectively solve Royal Road instances. Moreover, some unusual design solutions, competitive with standard approaches, are also proposed by the grammatical evolution framework.