Genetic Programming: An Introduction by Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, Frank D.

By Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, Frank D. Francone

Because the early Nineteen Nineties, genetic programming (GP)-a self-discipline whose aim is to let the automated iteration of desktop programs-has emerged as the most promising paradigms for speedy, effective software program improvement. GP combines organic metaphors gleaned from Darwin's idea of evolution with computer-science methods drawn from the sector of computing device studying to create courses which are able to adapting or recreating themselves for open-ended tasks.This particular creation to GP offers an in depth evaluation of the topic and its antecedents, with vast references to the broadcast and on-line literature. as well as explaining the elemental conception and critical algorithms, the textual content contains useful discussions overlaying a wealth of power purposes and real-world implementation concepts. software program execs desiring to appreciate and follow GP recommendations will locate this ebook a useful sensible and theoretical advisor.

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It is on observations such as these that general to specific transformation operators were devised. 8 Searches may also be conducted from the most specific to the most general. However, such searches are of limited interest because they tend to overfit with solutions that are far too specific. See Chapter 8. In a small problem domain, one could start a general to spe¬ cific search with all possible concept expressions that contained one feature. The next level of search could add one feature, expressed conjunctively, to each expression.

D. White and D. ), HANDBOOK OF INTELLIGENT CONTROL. NEURAL, FUZZY AND ADAPTIVE APPROACHES. Van Norstrand Reinhold, New York, NY, 1992. 6 Species and Sex 53 Looking back into the history of biology, it appears that wherever a phenomenon resembles learning, an instructive theory was first proposed to account for the underlying mechanisms. In every case, this was later replaced by a selective theory. Thus the species were thought to have developed by learning or by adaptation of individuals to the environment, until Darwin showed this to have been a selective process.

In short, the problem representation defines the set of all possible solutions to a problem that a particular ML system can find. " In other words, the representation of the problem defines the space of candidate solutions an ML system can find for a particular problem. A simple example illustrates this point. Suppose we wanted to predict the value of variable y from values of variable x. In the terms of the previous section, y is the output and x is the input. 1) The types of solutions this system could find would be very limited - all the system could do would be to optimize the parameters a, 6, and c.

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