An introduction to genetic algorithms
An introduction to genetic algorithms
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
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The ease with which the genetic algorithm can generate good solutions to challenging optimization problems has resulted in a tendency for researchers to overlook the easily gathered and largely untapped raw data present in the ancestral relationships that guide a population to convergence. This article introduces the notion of a lineage tree structure and associated ancestor count measure that reveals unexpected regularities when studying instances of a simple genetic algorithm applied to three disparate problems. Consequently, a series of explanatory models was constructed to identify the components of the underlying evolutionary mechanism that may be responsible for the commonalities observed. These components (exponential growth of member parentage and inbreeding caused by the appearance of duplicates and shared ancestries) place constraints on the number of ancestors that a solution may have; an insight that may prove valuable for future analysis of the behavior of the genetic algorithm.