(Copyright © 2000 Piero Scaruffi | Legal restrictions - Termini d'uso )
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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