Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Foundations of genetic programming
Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The simple genetic algorithm and the walsh transform: Part ii, the inverse
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
Genetic algorithms on NK-landscapes: effects of selection, drift, mutation, and recombination
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
| Hi-index | 0.00 |
Recombination is an important operator in the evolution of biological organisms and has also played an important role in Evolutionary Computation. In neither field however, is there a clear understanding of why recombination exists and under what circumstances it is useful. In this paper we consider the utility of recombination in the context of a simple Genetic Algorithm (GA). We show how its utility depends on the particular landscape considered. We also show how the facility with which this question may be addressed depends intimately on the particular representation used for the population in the GA, i.e., a representation in terms of genotypes, Building Blocks or Walsh modes. We show how, for non-epistatic landscapes, a description in terms of Building Blocks manifestly shows that recombination is always beneficial, leading to a "royal road" towards the optimum, while the contrary is true for highly epistatic landscapes such as "needle-in-a-haystack".