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It s a delicate balance, but many of these algorithms take steps to prevent this kind of shortsightedness Some of these strategies have exciting names like hill climbing, simulated annealing, and ant colony optimization But you ll be focusing in one particular strategy that s received a lot of popular press since its inception Genetic algorithms are based on the observed process of biological evolution Solutions are randomly generated and then pitted against each other The most promising solutions are kept for the next round where they will reproduce in order to produce new solutions, which then compete in the next round Capturing the essence of this biological process makes genetic algorithms a fascinating topic for beginners and experts alike It s easy to be awed as you watch evolution play out before your very eyes In this chapter, you ll put together a flexible system for building genetic algorithms.

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However, for the Ziya graphs, let s try something different by graphing our exercise results instead Open our exercises controller (/app/controllers/exercises_controllerrb), and add Ziya to this controller We do that by first requiring the Ziya library and then including it within our controller to keep its scope local: require 'ziya' class ExercisesController < ApplicationController include Ziya before_filter :login_required (..lines omitted..) end With Ziya added to our exercises controller, let s take a quick look at how we build a Ziya graph before we implement one Within the controller, we instantiate a Ziya chart by creating a new object of the graph type that we wish to create: graph = Ziya::Charts::Barnew(nil, "My Cool Graph", 'bar_chart').

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The Jump to Program menu gives you a quick way to move between recordings. It does this by presenting basically the same user interface as the watch recordings screen but in a much more compressed form. You ll see a submenu that lists all the titles that are currently recorded, and you can then select an episode of a title to jump to.

Then you ll return to the coin simulation and use genetic algorithms to explore some of the larger variants of the problem (like the optimal 4- and 5-coin variants) With any luck, you ll get some interesting insights into evolution along the way..

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So what, exactly, does a genetic algorithm look like To start with, you need an initial population You can think of it as having four phases: initialization, selection, recombination, and termination (although the selection and recombination phases are executed multiple times) However, in order to start, you ll need to be able to generate an initial population You can think of the population as the life-forms in our ecosystem (although, to be accurate, the population is really these life-forms genetic material) Which brings us to an interesting question: what would this collection of genetic material (genome) look like inside a computer In many biological systems genetic information is stored using deoxyribonucleic acid (DNA) DNA is built from four nucleotides named adenine (A), cytosine (C), guanine (G), and thymine (T) In the right light, DNA starts to look a lot like a number in base four.

This object takes three optional parameters: License: XML/SWF is free to download and use, but the unlicensed version does have a few limitations; for example, clicking a chart will take the user to the XML/SWF charts homepage You can purchase a license for your application from their page and place the license here to eliminate that Chart Name: This is the name of your chart Chart_id: This is a YAML-based configuration file stored in /public/charts that we can use to radically customize our graph We ll look into this shortly Ziya supports a very large number of possible charts that you can create such as bar, line, pie, area, and stacked charts as well as 3-D graphs The best place to learn about all the possible options is at the online Rdocs at http://ziyaliquidrailcom/rdoc/indexhtml.

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