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Belew, R K, & Mitchell, M (Eds) (1996) Adaptive individuals in evolving populations: Models and algorithms Reading, MA: Addison-Wesley Bledsoe, W (1961) The use of biological concepts in the analytical study of systems Proceedings of the ORSA-TIMS National Meeting, San Francisco Booker, L B, Goldberg, D E, & Holland, J H (1989) Classifier systems and genetic algorithms Artificial Intelligence, 40, 235-282 Box, G (1957) Evolutionary operation: A method for increasing industrial productivity Jountal of the Royal Statistical Society, 6(2), 81-101 Cohoon, J P, Hegde, S U, Martin, W N, & Richards, D (1987) Punctuated equilibria: A parallel genetic algorithm Proceedings of the Second International Conference on Genetic Algorithms (pp 148-154) DeJong, K A (1975) An analysis of behavior of a class of genetic adaptive systems (PhD dissertation) University of Michigan DeJong, K A, Spears, W M, & Gordon, D F (1993) Using genetic algorithms for concept learning Machine Learning, 13, 161-188 Folgel, L J, Owens, A J, & Walsh, M J (1966) Artificial intelligence through simulated evolution New York: John Wiley & Sons Fogel, L J, & Atmar, W (Eds) (1993) Proceedings of the Second Annual Conference on Evolutionary Programming Evolutionary Programming Society Forrest, S (1993) Genetic algorithms: Principles of natural selection applied to computation Science, 261, 872-878 French, R, & Messinger A (1994) Genes, phenes, and the Baldwin effect: Learning and evolution in a simulated population In R Brooks and P Maes (Eds), ArtiJicial Life IV Cambridge, MA: MIT Press Goldberg, D (1989) Genetic algorithms in search, optimization, and machine learning Reading, MA: Addison-Wesley Goldberg, D (1994) Genetic and evolutionary algorithms come of age Communicationsof the ACM, 37(3), 113-1 19 Green, D P, & Smith, S F (1993) Competition based induction of decision models from examples Machine Learning, 13,229-257 Grefenstette, J J (1988) Credit assignment in rule discovery systems based on genetic algorithms Machine Learning, 3, 225-245 Grefenstette, J J (1991) Lamarckian learning in multi-agent environments In R Belew and L Booker (Eds), Proceedings of the Fourth International Conference on Genetic Algorithms San Mateo, CA: Morgan Kaufmann Hart, W, & Belew, R (1995) Optimization with genetic algorithm hybrids that use local search In R Below and M Mitchell (Eds), Adaptive individuals in evolving populations: Models and algorithms Reading, M A : Addison-Wesley Harvey, I (1993) The puzzle of the persistent question marks: A case study of genetic drift In Forrest (Ed), Proceedings of the Fzfth International Conference on Genetic Algorithms San Mateo, CA: Morgan Kaufmann Hinton, G &, & Nowlan, S J (1987) How learning can guide evolution Complex Systems, 1, 495-502 Holland, J H (1962) Outline for a logical theory of adaptive systems Journal of the Association for Computing Machinery, 3, 297-314 Holland, J H (1975) Adaptation in natural and art$cial systems University of Michigan Press (reprinted in 1992 by MIT Press, Cambridge, MA).

Next, the JavaScript code:

var ceil_button = documentgetElementById("getceiling"); ceil_buttononclick = function() { var num1= windowprompt("Enter a number",""); var theceil= Mathceil(num1); windowalert("The ceiling of "+num1+ " is "+theceil); };

.

This script displays an alert that states the ceiling of the number entered by the viewer The following illustration shows the result of this in the browser when the viewer enters 455 at the prompt:

.

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The floor() Method The floor() method is like the ceil() method, but it goes the opposite way The floor() method returns the largest integer less than or equal to the parameter sent to the method This rounds down to the next lowest integer, unless the parameter is an integer already In that case, it returns the same integer since it is already equal to an integer Basically, this method just removes the decimal part of a number and leaves the integer as the result For instance, Mathfloor(1223); will return 12 and Mathfloor(12); will also return 12 You can use the floor() method in the same way the ceil() method was used in the preceding section by assigning the result to a variable The round() Method The round() method works like the previous two methods, but instead rounds the number entered as the parameter to the nearest integer whether it is greater or less than the number Any number having the decimal portion s value at 5 or greater will be rounded up, while any decimal portion with a value less than 5 is rounded down The 5 cutoff is strict, so Mathround(1249999999); would return 12 even though your tendency may be to round it up The random() Method The random() method is very useful for creating scripts that require random integers It returns a random number between 0 and 1 This means that you get a number with a decimal that can be quite long and not useful on its own For instance, it might return something like 036511165498095293 To get a random integer that you can use, you need to do some things to get the type of value that you want to use

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