An Introduction to Genetic Algorithms (Complex Adaptive Systems)

An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Melanie Mitchell

Language: English

Pages: 221

ISBN: 0262631857

Format: PDF / Kindle (mobi) / ePub


Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

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that grammar. A grammar is a set of rules that can be applied to produce a set of structures (e.g., sentences in a natural language, programs in a computer language, neural network architectures). A simple example is the following grammar: Here S is the start symbol and a nonterminal, a and b are terminals, and µ is the empty−string terminal.(S ’ µ means that S can be replaced by the empty string.) To construct a structure from this grammar, start with S, and replace it by one of the allowed

survive, whereas the former selects traits only on the basis of what attracts potential mates. However, the possession of either kind of trait accomplishes the same thing: it increases the likelihood that an organism will reproduce and thus pass on the genes for the trait to its offspring. There are many open questions about how sexual selection works, and most of them are hard to answer using traditional methods in evolutionary biology. How do particular preferences for traits (such as elaborate

bit string. The "genes" are either single bits or short blocks of adjacent bits that encode a particular element of the candidate solution (e.g., in the context of multiparameter function optimization the bits encoding a particular parameter might be considered to be a gene). An allele in a bit string is either 0 or 1; for larger alphabets more alleles are possible at each locus. Crossover typically consists of exchanging genetic material between two singlechromosome haploid parents. Mutation

"semantics" of the original chromosome. To use inversion in GAs, we have to find some way for the functional interpretation of an allele to be the same no matter where it appears in the string. For example, in the chromosome encoding a cellular automaton (see section 2.1), the leftmost bit under lexicographic ordering is the output bit for the neighborhood of all zeros. We would want that bit to represent that same neighborhood even if its position were changed in the string under an inversion.

as follows: 1. Start with a randomly generated population of n l−bit chromosomes (candidate solutions to a problem). 2. Calculate the fitness ƒ(x) of each chromosome x in the population. 3. Repeat the following steps until n offspring have been created: a. Select a pair of parent chromosomes from the current population, the probability of selection being an increasing function of fitness. Selection is done "with replacement," meaning that the same chromosome can be selected more than once to

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