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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Offered precisely the same deal. Both Alice and Bob know that if neither testify against the other they can be convicted only on a lesser charge for which they will each get 2 years in jail. Should Alice "defect" against Bob and hope for the suspended sentence, risking a 4−year sentence if Bob defects? Or should she "cooperate" with Bob (even though they cannot communicate), in the hope that he will also cooperate so each will get only 2 years, thereby risking a defection by Bob that will send.
Their fellow population members and thus tended to die out, but after about 10–20 generations the trend started to reverse: the GA discovered strategies that reciprocated cooperation and that punished defection (i.e., variants of TIT FOR TAT). These strategies did well with one another and were not completely defeated by less cooperative strategies, as were the initial cooperative strategies. Because the reciprocators scored above average, they spread in the population; this resulted in.
Left−hand side in two or more different rules, only the first such rule is included in the grammar (Hiroaki Kitano, personal communication). The fitness of a grammar was calculated by constructing a network from the grammar, using back−propagation with a set of training inputs to train the resulting network to perform a simple task, and then, after training, measuring the sum of the squares of the errors made by the network on either the training set or a separate test set. (This is similar to.
Theoretical Foundations of Genetic Algorithms Nix and Vose, we will enumerate this for each possible string. The number of ways of choosing Z0,j occurrences of string 0 for the Z0,j slots in population j is Selecting string 0 Z0,j times leaves n Z 0,j positions to fill in the new population. The number of ways of placing the Z1,j occurrences of string 1 in the n Z 0,j positions is Continuing this process, we can write down an expression for all possible ways of forming population Pj from a set.
Preferentially: the segments exchanged between the two parents always contain the endpoints of the strings. 128 Chapter 4: Theoretical Foundations of Genetic Algorithms To reduce positional bias and this "endpoint" effect, many GA practitioners use two−point crossover, in which two positions are chosen at random and the segments between them are exchanged. Two−point crossover is less likely to disrupt schemas with large defining lengths and can combine more schemas than single−point crossover.