Microsoft word - said - genetic algorithms v10.doc

A comparison of “On Genetic Algorithms and Their Application” (Yasmin Said) p. 359-360 and Sources on Genetic Algorithms (Ev. Comp FAQ, M Mitchell, J Holland)
Regular font indicates substantially close wording between the two sources, italic represent paraphrased sections, bold represents significant departures of Said
from sources, and bold underline represent points of outright contradiction. Paragraphs have been reformatted for easy comparison. Within sections of close
wording, identical phrases (ID) are highlighted in cyan, trivial changes (TC) with yellow . Changes resulting in issues are underlined.

Yasmin Said, On Genetic Algorithms and Their Application, p. 359
Evolutionary Computing FAQ (Intro) [N.B. Capitilized links rendered
in lower-case for readability]
Introduction – para 1
Paragraph 1
Under the umbrella of evolutionary computation (EC) also referred to Evolutionary algorithm is an umbrella term used to describe computer- based problem solving systems which use computational models of some of the known mechanisms of evolution as key elements in their design and implementation. A variety of evolutionary algorithms have been proposed. Wegman para 2
are the areas of evolutionary programming (EPs) and evolution The major ones are: genetic algorithms (see Q1.1), evolutionary programming (see Q1.2), evolution strategies (see Q1.3), classifier systems (see Q1.4), and genetic programming (see Q1.5). Each of these methods of evolutionary computation simulate the They all share a common conceptual base of simulating the evolution of process of evolution through the mutation, selection and/or individual structures via processes of selection, mutation, and reproduction processes and rely on perceived performance of individual reproduction. The processes depend on the perceived performance of the structures assigned by the environment. individual structures as defined by an environment. Paragraph 2
Evolutionary algorithms support population structures which progress More precisely, EAs maintain a population of structures, that evolve to the rules of selection using genetic operators. according to rules of selection and other operators, that are referred to as "search operators", (or genetic operators), such as recombination and Genetic operators determine which structures will move on to the next In essence, the individuals in the population obtain a degree of fitness Each individual in the population receives a measure of its fitness from the environment. Reproduction concentrates on high fitness in the environment. Reproduction focuses attention on high fitness individuals, thus exploiting (cf. exploitation) the available fitness [Two paragraphs omitted] Deep Climate A comparison of “On Genetic Algorithms and Their Application” by Yasmin Said and sources on Genetic Algorithms 2 of 4
Said, p. 360
Melanie Mitchell, An introduction to genetic algorithms, p. 2.
Paragraph 1
In the middle of the twentieth century some computer scientists In the 1950s and the 1960s several computer scientists independently studied worked on evolutionary systems with the notion that this will yield evolutionary systems with the idea that evolution could be used as an to an optimization mechanism for an array of engineering queries optimization tool for engineering problems. … GAs were invented and developed by John Holland, his students Genetic algorithms (GAs) were invented by John Holland in the 1960s and and his colleagues at the University of Michigan. were developed by Holland and his students and colleagues at the University of Michigan in the 1960s and the 1970s. In contrast with evolution strategies His team's original intentions were not to create and evolutionary programming, Holland's original goal was not to design algorithms, but instead to determine exactly how algorithms to solve specific problems, but rather to formally study the adaptation occurs in nature and then develop ways phenomenon of adaptation as it occurs in nature and to develop ways in that natural adaptation might become a part of computer systems. which the mechanisms of natural adaptation might be imported into computer Holland's book Adaptation in Natural and Artificial Systems (1975) Holland's 1975 book Adaptation in Natural and Artificial Systems presented set forth the lexicon from which all further dialogue concerning the genetic algorithm as an abstraction of biological evolution and gave a GAs would be developed. In essence, this theoretical framework theoretical framework for adaptation under the GA. provided the point of reference for all work on genetic algorithms up until recently whereupon it has taken on a new direction, given GA, he stated, moves one population of bits (chromosomes and Holland's GA is a method for moving from one population of "chromosomes" genes) to a new population using a type of "natural selection" along (e.g., strings of ones and zeros, or "bits") to a new population by using a kind with genetic operators of crossover, mutation and inversion (all of "natural selection" together with the genetics−inspired operators of These operators determine which chromosomes are the fittest and The selection operator chooses those chromosomes in the population that will thus able to move on. Although some less fit chromosomes do move be allowed to reproduce, and on average the fitter chromosomes produce forward, on average the most fit chromosomes produce more more offspring than the less fit ones. offspring than their less fit counterparts. Deep Climate A comparison of “On Genetic Algorithms and Their Application” by Yasmin Said and sources on Genetic Algorithms 3 of 4
Biological recombination occurs between these chromosomes and Crossover exchanges subparts of two chromosomes, roughly mimicking chromosomal inversion further completes the process of providing as biological recombination between two single−chromosome ("haploid") many type possible of recombination or "crossover". organisms; mutation randomly changes the allele values of some locations in the chromosome; and inversion reverses the order of a contiguous section of the chromosome, thus rearranging the order in which genes are arrayed. This remarkable quality that genetic algorithms have of focusing their This remarkable ability of genetic algorithms to focus their attention on the "fittest" parts of a solution set in a population is attention on the most promising parts of a solution space is directly related to their ability to combine strings which contain a direct outcome of their ability to combine strings containing partial solutions. First, each string in the population is ranked to partial solutions. First, each string in the population is evaluated to determine the execution of the tactic that it predetermines. Second. determine the performance of the strategy that it encodes. Second, the higher-ranking strings mate in couplets. When the two strings the higher-ranking strings mate. Two strings assemble, a random point along the strings is selected and the line up, a point along the strings is selected at random and the portions adjacent to that point are swapped to produce two offspring: portions to the left of that point are exchanged to produce two offspring: one which contains the encoding of the first string up to the crossover one containing the symbols of the first string up to the crossover point, those of the second beyond it, and the other containing the point and those of the second beyond it, and the other containing the Biological chromosomes perform the function of crossover when Biological chromosomes cross over one another when two zygotes and gametes meet and so the process of crossover in genetic gametes meet to form a zygote, and so the process of crossover in genetic algorithms is designed to mimic its biological nominative. Successive algorithms does in fact closely mimic its biological model. The offspring do offspring do not replace the parent strings; rather they replace low- not replace the parent strings; instead they replace low-fitness strings, fitness ones, which are discarded information at each generation in which are discarded at each generation so that the total population remains order that the population size is maintained. Deep Climate A comparison of “On Genetic Algorithms and Their Application” by Yasmin Said and sources on Genetic Algorithms 4 of 4
1. Yasmin H. Said, Data mining and data visualization, ed. Calyampudi Radhakrishna Rao, Edward J. Wegman, Jeffrey L. Solka, Elesevier, 2005. Online at: 2. Evolutionary Computing FAQ (from "An Overview of Evolutionary Computation", ECML: European Conference on Machine Learning Machine Learning [ECML93], 442-459. Online at: 3. Melanie Mitchell, An introduction to genetic algorithms, MIT Press, 1998. Online at: 4. John H. Holland, “Genetic Algorithms”, Scientific American, July 1992. Online at:



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