Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Artificial Life: A Report from the Frontier Where Computers Meet Biology
Artificial Life: A Report from the Frontier Where Computers Meet Biology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Proceedings of the 6th International Conference on Evolutionary Programming VI
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Proceedings of the 7th International Conference on Evolutionary Programming VII
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
IBM Journal of Research and Development
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A note on representations and variation operators
IEEE Transactions on Evolutionary Computation
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
Schema processing under proportional selection in the presence ofrandom effects
IEEE Transactions on Evolutionary Computation
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All of science relies on past experimentation and hypotheses. Unfortunately, the science of evolutionary computation is hampered by a general lack of awareness of many early efforts in the field. This paper offers a review of one such contribution from 1967 which employed self-adaptation, co-evolution, and assessed the utility of recombination in various settings. The conclusions, reconfirmed in recent literature, indicate that recombination (uniform or one-point crossover) is best applied in non-epistatic settings. Theoretical analysis supported the experimental findings and now raises questions concerning common applications of schema theory to describe the behavior of evolutionary algorithms.