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Evolution-based computational approaches have been explored since the early days of computer science (eg, Box 1957 and Bledsoe 1961) Several different evolutionary approaches were introduced during the 1960s and have been further explored since that time Evolution strategies, developed by Rechenberg (1965, 1973) to optimize numerical parameters in engineering design, were followed up by Schwefel (1975, 1977, 1995) and others Evolutionary programming, developed by Folgel, Owens, and Walsh (1966) as a method for evolving finite-state machines, was followed up by numerous researchers (eg, Fogel and Atmar 1993) Genetic algorithms, introduced by Holland (1962, 1975) included the notion of maintaining a large population of individuals and emphasized crossover as a key operation in such systems Genetic programming, introduced by Koza (1992), applies the search strategy of genetic algorithms to hypotheses consisting of computer programs As computer hardware continues to become faster and less expensive, interest in evolutionary approaches continues to grow One approach to using GAS to learn sets of rules was developed by K DeJong and his students at the University of Pittsburgh (eg, Smith 1980) In this approach, each rule set is one member in the population of competing hypotheses, as in the GABIL system discussed in this chapter A somewhat different approach was developed at University of Michigan by Holland and his students (Holland 1986), in which each rule is a member of the population, and

Other than its difference in calculations, you can use it in the same general way that you used the other two-parameter methods by assigning the result to a variable and then using the variable in a script As an example, you could use the following script, starting with the HTML code:

.

Next, the JavaScript code:

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the population itself is the rule set A biological perspective on the roles of mutation, inbreeding, cross-breeding, and selection in evolution is provided by Wright (1977) Mitchell (1996) and Goldberg (1989) are two textbooks devoted to the subject of genetic algorithms Forrest (1993) provides an overview of the technical issues in GAS, and Goldberg (1994) provides an overview of several recent applications Koza's (1992) monograph on genetic programming is the standard reference for this extension of genetic algorithms to manipulation of computer programs The primary conference in which new results are published is the International Conference on Genetic Algorithms Other relevant conferences include the Conference on Simulation of Adaptive Behavior, the International Conference on Artijicial Neural Networks and Genetic Algorithms, and the IEEE International Conference on Evolutionary Computation An annual conference is now held on genetic programming, as well (Koza et al 1996b) The Evolutionary Computation Journal is one source of recent research results in the field Several special issues of the journal Machine Learning have also been devoted to GAS

var pow_button = documentgetElementById("getpow"); pow_buttononclick = function() { var num1 = windowprompt("Enter a base number",""); var num2 = windowprompt("What power should we set it to (a number) ",""); var theresult = Mathpow(num1,num2); windowalert(num1+" to the power of "+num2+" is "+theresult); };

Using this code, if the viewer enters 2 as the first number and 3 when asked for a power, the script will compute the result of 2 to the 3rd power The viewer then is given an alert showing the answer Clicking the button in the HTML code is how the viewer starts the function The result of this script when the numbers 2 and 3 are input is shown here:

In this chapter, we will cover the special structured cabling techniques you must employ to support gigabit and 10 Gb speeds In the local area network (LAN), Ethernet is clearly the primary topology for these speeds, so we will concentrate on the structured cabling standards that support Gigabit Ethernet and 10 Gigabit Ethernet ATM at OC-12c is another topology that is available, although it is not quite gigabit speed Likewise, OC-192 is virtually 10 Gb, too, but these technologies can be supported in the LAN with the same techniques we use for comparable Ethernet speeds Gigabit structured cabling techniques will be covered first, and the 10 Gb techniques will be covered later in the chapter Gigabit refers to a data transfer speed of 1,000,000,000 bits per second, or 1 Gbps The corresponding Ethernet technology meets this definition exactly; it operates at 1 Gbps Technically, ATM at OC-12c is at 622 Mbps data rate, so it is about two-thirds gigabit speed In fact, the standards revision TIA-568-A (and now TIA568-C as well) incorporates wiring technologies that accommodate both of these gigabit-class speeds This standard specifies the provision of two cables to each workstation outlet, one of which may be fiber Although it is up to the cabling system designer to include the fiber, the provision of multimode fiber makes it easy to implement either of the common gigabit-class technologies, Gigabit Ethernet or ATM/OC-12c But, what about copper Well, thanks to the tight specifications of this standard, we are now using Category 5e and higher cabling for gigabit copper networks for both Ethernet and ATM-622 Network speeds go up by a factor of 10 about every five years (Fig 121) 10 Gigabit refers to a data transfer speed of 10,000,000,000 bits per second, or 10 Gbps In the LAN, 10 GigE (as it is often called) Ethernet operates at that.

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