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Holland, J H (1986) Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems In R Michalski, J Carbonell, & T Mitchell (Eds), Machine learning: An artijicial intelligence approach (Vol 2) San Mateo, CA: Morgan Kaufmann Holland, J H (1989) Searching nonlinear functions for high values Applied Mathematics and Computation, 32, 255-274 Janikow, C Z (1993) A knowledge-intensive GA for supervised learning Machine Learning, 13, 189-228 Koza, J (1992) Genetic programming: On the programming of computers by means of natural selection Cambridge, MA: MIT Press Koza, J R (1994) Genetic Programming 11: Automatic discovery of reusable programs Cambridge, MA: The MIT Press Koza, J R, Bennett 111, F H, Andre, D, & Keane, M A (1996) Four problems for which a computer program evolved by genetic programming is competitive with human performance Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (pp 1-10) IEEE Press Koza, J R, Goldberg, D E, Fogel, D B, & Riolo, R L (Eds) (1996b) Genetic programming 19%: Proceedings of the First Annual Conference Cambridge, MA: MIT Press Machine Learning: Special Issue on Genetic Algorithms (1988) 3:2-3, October Machine Learning: Special Issue on Genetic Algorithms (1990) 5:4, October Machine karning: Special Issue on Genetic Algorithms (1993) l3:2,3, November Mitchell, M (1996) An introduction to genetic algorithms Cambridge, MA: MIT Press O'Reilly, U-M, & Oppacher, R (1994) Program search with a hierarchical variable length representation: Genetic programming, simulated annealing, and hill climbing In Y Davidor et al (Eds), Parallel problem solving from nature-PPSN I11 (Vol 866) (Lecture notes in computer science) Springer-Verlag Rechenberg, I (1965) Cybernetic solution path of an experimental problem Ministry of aviation, Royal Aircraft Establishment, UK Rechenberg, I (1973) Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biolgischen evolution Stuttgart: Frommann-Holzboog Schwefel, H P (1975) Evolutionsstrategie und numerische optimiemng (PhD thesis) Technical University of Berlin Schwefel, H P (1977) Numerische optimierung von computer-modellenmittels der evolutionsstrategie Basel: Birkhauser Schwefel, H P (1995) Evolution and optimum seeking New York: John Wiley & Sons Spiessens, P, & Manderick, B (1991) A massively parallel genetic algorithm: Implementation and first analysis Proceedings of the 4th International Conference on Genetic Algorithms (pp 279-286) Smith, S (1980) A learning system based on genetic adaptive algorithms (PhD dissertation) Computer Science, University of Pittsburgh Stender, J (Ed) (1993) Parallel genetic algorithms Amsterdam: IOS Publishing Tanese, R (1989) Distributed genetic algorithms Proceedings of the 3rd International Conference on Genetic Algorithms (pp 434-439) Teller, A, & Veloso, M (1994) PADO: A new learning architecture for object recognition In K Ikeuchi & M Veloso (Eds), Symbolic visual learning @p 81-116) Oxford, England: Oxford Univ Press Turney, P D (1995) Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm Journal of Al Research, 2, 369-409 http://wwwcswashingtonedu/ research/jair/homehtmI.

Random Integers To get a random integer, the first thing you will want to do is to make the result have a greater range of values so that you are not stuck between 0 and 1 To get a greater range of values, you can multiply the result of the random() method by an integer to create a larger range Like an array, the range would begin counting from 0; so, to get a range of five possible integers, you would multiply the result by 5 The following code shows how you can do this:

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Gigabit Ethernet is formally specified in the supplement to the base 8023 standard known as IEEE 8023z, which includes the fiber implementations, and in IEEE 8023ab for twisted-pair copper It is sometimes referred to as 1000Base-X The X signifies a physical layer based on the ANSI X3230 Fibre Channel1 standard, in the same manner that 100Base-X fiber modes were based on the FDDI physical layer The choice of Fibre Channel signaling was made simply to expedite the introduction of the Gigabit Ethernet technology At the time of first consideration of expanding Ethernet operation to the gigabit range, Fibre Channel existed as an accepted standard More importantly, integrated circuits existed to implement the signaling, so it was very time- and cost-effective to utilize the same basic circuitry The signaling rate was modified to allow for an 8-line by 125 Mbps interface to achieve a data rate of exactly 1000 Mbps The actual medium-independent layer of Gigabit Ethernet is the 8-bit-wide gigabit media independent interface (GMII), corresponding to the MII in Fast Ethernet and the AUI in standard Ethernet Gigabit Ethernet is topologically similar to Fast Ethernet (100BaseT) All workstations connect directly to a switch or hub, and collision domains are tightly controlled to allow the collision-detection mechanism to operate properly As a matter of fact, shared-medium operation of Gigabit Ethernet is virtually absent, as Gigabit Ethernet switches are used in most hub-type applications In essence, the switch is an OSI Layer 2 bridge that operates much like its lower speed, multiport counterparts Gigabit Ethernet was originally specified as a fiber-optic topology, because the bulk of installed cabling at the time was only Category 3 Cat 3 simply was not capable of carrying the higher symbol rates needed to support 1 Gbps data Today, all of the newly installed cabling is Cat 5e or above, which makes a copper implementation possible.

var rand_num= Mathrandom()*5;

Tumey, P D, Whitley, D, & Anderson, R (1997) Evolutionary Computation Special issue: The Baldwin effect, 4(3) Cambridge, MA: MIT Press http://www-mitpressmiteduljmlscatalog/evolution-abstracts/evolhtml Whitley, L D, & Vose, M D (Eds) (1995) Foundations of genetic algorithms 3 Morgan Kaufmann Wright, S (1977) Evolution and the genetics of populations Vol 4: Variability within and among Natural Populations Chicago: University of Chicago Press Zbignlew, M (1992) Genetic algorithms + data structures = evolution programs Berlin: SpringerVerlag

This gets the result between 0 and 4, but does not give you an integer yet The number could still come out as a long decimal number To get an integer between 0 and 4, you need to find a way to make all of these decimal numbers convert to integers Recall that earlier you ran through three methods, floor(), ceil(), and round(), that converted numbers to integers in various ways The floor() method is the one you will choose here because it simply removes the decimal places after the integer and gives you the integer portion of the number To use the floor() method, you could write the following code:

var rand_num = Mathrandom()*5; var rand_int = Mathfloor(rand_num);

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