Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Evolving programmers: the co-evolution of intelligent recombination operators
Advances in genetic programming
Two self-adaptive crossover operators for genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Self-adaptation in evolving systems
Artificial Life
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the European Conference on Genetic Programming
Evolutionary Design of an X-Band Antenna for NASA's Space Technology 5 Mission
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Evolving Evolutionary Algorithms with Patterns
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
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
Evolving strategies for updating pheromone trails: a case study with the TSP
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Towards the development of self-ant systems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Designing pheromone update strategies with strongly typed genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Evolving evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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
Computers & Mathematics with Applications
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Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. 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 generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.