A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
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 algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Evolving programmers: the co-evolution of intelligent recombination operators
Advances in genetic programming
Self-adaptation in evolving systems
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Evolving Evolutionary Algorithms with Patterns
Soft Computing - A Fusion of Foundations, Methodologies and Applications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines
Evolving black-box search algorithms employing genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.