An introduction to genetic algorithms
An introduction to genetic algorithms
Evolutionary computation
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
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
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Due to the lot of different Genetic Algorithm variants, encodings, and attacked problems, very little general theory is available to explain the internal functioning of Genetic Algorithms. Consequently it is very difficult for researchers to find a common language to document quality improvements of newly developed algorithms. In this paper the authors present a new Allele Meta-Model enabling a problem-independent description of the search process inside Genetic Algorithms. Based upon this meta-model new measurement values are introduced that can be used to measure genetic diversity, genetic flexibility, or optimization potential of an algorithm's population. On the one hand these values help Genetic Algorithm researchers to understand algorithms better and to illustrate newly developed techniques more clearly. On the other hand they are also meaningful for any GA user e.g. to tune parameters or to identify performance problems.