David Goldberg:
GENETIC ALGORITHMS (Addison Wesley, 1989)

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This is the book that explained what Holland's theories were all about. Goldberg defines genetic algorithms as "search algorithms based on the mechanics of natural selection and natural genetics". Unlike most optimization methods, that work from a single point in the decision space and employ a transition method to determine the next point, genetic algorithms work from an entire "population" of points simultaneously, trying many directions in parallel and employing a combination of several genetically-inspired methods to determine the next population of points.
Goldberg focuses on efficiency issues and possible applications.
Simple algorithms such as reproduction (that copies chromosomes according to a fitness function), crossover (that switches segments of two chromosomes) and mutation are discussed, as well as more complex algorithms such as dominance (a genotype-to-phenotype mapping), diploidy (pairs of chromosomes) and abeyance (shielded against overselection); inversion (the primary natural mechanism for recoding a problem, by switching two points of a chromosome); and many micro-operators.
A classifier system is a machine learning system that learns syntactically rules (or "classifiers") to guide its performance in the environment. A classifier system consists of three main components: a production system, a credit system (such as the "bucket brigade") and a genetic algorithm. Goldberg verifies an important properties of Holland's classifiers: the trend to create "standard hierarchies", in which a general rule covers normal situations but many exception rules take over in those situations where the default rule would not work.

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