David Powell
Professor Kenneth C. Thompson
ENGH 302-SN2
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1 August 2015
Is Evolutionary Computation a Form of Machine Learning?
Creating sophisticated data models is the focus of a large portion of the computer science discipline. The deep-seated need for advanced systems in today’s world forces programmers to think outside the box when developing unique and interesting forms of computing. ————Add fluff if necessary? Feedback appreciated————
Evolutionary computation is a form of computing which attempts to solve complex problems related to machine learning. In this form of computing, the system is designated a certain task by the programmer. This task is simply whatever the desired outcome might be. In evolutionary computation, the main idea is that the computer will continually attempt to achieve its goal, until it finally gets it right. This could take thousands of attempts, where the computer throws new data at the problem each time, hoping it works. Every time the computer comes up with a potential solution, the only reason this new method would have the potential to work, is if it was different from previous attempts. Simply doing the same thing over and over would yield the same result: failure. Thus, the computer must change its approach each time it tries to solve the problem it has been tasked with. The techniques employed when implementing these changes is what makes evolutionary computation so compelling and unique.
The first major step in evolutionary computation is to create a handful of initial attempts.
Each one would be ever so slightly different from the next, because parts of the models would be developed using random numbers. As a result, each model in this first batch would perform

its task differently, and with varying degrees of success. After everyone in the first batch
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Evolutionary computation’s vernacular is not complicated. In fact, it actually makes it easier to understand what is going on. Generations, as they are denoted, are made up of parents, who then produce children. The children give it their best shot, and then have kids of their own. The reason the population is getting better all the time, is because the computer ranks the members of each generation after they attempt to solve the problem. The computer’s selection is what guides the population towards its end goal, and is effectively the reason this form of computation is able to deliver the desired results.
This form of computation came about because of the biological theory of evolution. In
1859, Charles Darwin published On the Origin of Species , a comprehensive work which described gradual change in a species, through a process of natural selection (CITE THIS). It is possible for random mutations to occur during cellular reproduction, which change the way the organism is made. Some mutations would benefit the organisms, and some would harm them.
These minute differences in the population gave some members of the species an upper-hand, meaning they were more likely to reproduce, and pass their own genetic code (mutations and all) down to the next generation. Over millions of years, the species would gradually evolve to change with its environment. This is biological evolution by process of natural selection.

Actual argumentative paragraph. Last paragraph of intro. Finally get to bring in machine
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Talk about different definitions of machine learning
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Evolutionary computation is a very sophisticated form of machine learning, because it is self correcting, incredibly accurate, and it can scale with new data.
The idea of self-correction describes a machine’s ability to fix itself once it has made a mistake. — more— The algorithm must be able to instruct itself on what to change, without input from the outside world. — more—
Why it is important in machine learning:
Self correction could be thought of as the knowledge that a mistake was made, and then fixing that error in the future. Somehow, the machine needs to have the ability to recognize what a mistake is, figure out a better alternative, and then implement that new idea in its coming performances. This is essentially the essence of what it means to learn something. Recognize you’re not operating in the optimal way, and then fix yourself until you do.
Evolutionary computation also has proven to produce very accurate data and models.

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This level of accuracy is very impressive when it comes to machine learning. Many other forms of machine learning require a programmer to sit down and fully author all the rules a machine must follow. With evolutionary computation however, once the initial scaffolding is constructed, the machine is permitted to run away by itself. It would be very difficult for a programmer to sit down and write a program that could accurately simulate World War III, for example. But an evolutionary computational algorithm could achieve an accurate simulation given enough time, without much thought from the programmer about how the war would actually play out in real life.
——more of course——
Scaling with data is another important aspect when thinking about machine learning.
Luckily, data scaling is one of evolutionary computation’s main features. Over time, it is constantly reevaluating its current data set to randomly create new data. The next generation then then incorporate that new data into its ensuing computations. With each generation, new data is thrown at the machine, and it is the analysis of that data which directly leads to the eventual growth evolutionary computation promises.
—A lot more—-
—Conclusion—

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Works Cited
Xie, Yu, Chunxia Zhao, Haofeng Zhang, and Debao Chen. "DEGSO: Hybrid Group Search
Optimizer with Differential Evolution Operator." International Journal of Signal
Processing, Image Processing and Pattern Recognition 7.6 (2014). Print.
Lanza-Gutierrez, Jose, Juan Gomez-Pulido, and Miguel Vega-Rodriguez. "A New Realistic
Approach for the Relay Node Placement Problem in Wireless Sensor Networks by
Means of Evolutionary Computation." Ad Hoc & Sensor Wireless Networks 26 (2015).
Print.
Roebber, Paul J. "Adaptive Evolutionary Programming." Mon. Wea. Rev. Monthly Weather
Review 143 (2015): 1497-505. EbscoHost . Web. 30 July 2015.
Eiben, Agoston E., and J. E. Smith. Introduction to Evolutionary Computing . Second ed. Berlin:
Springer-Verlag, 2003. Print.
Fogel, David. "What Is Evolutionary Computation?" York University . 2000. Web. 11 July 2015.
<https://wiki.eecs.yorku.ca/course_archive/2013-14/F/4403/_media/ec1.pdf>.
Is dictionary a book?
Not a comparison paper. Too much biology?

Starting sentences with
“But”
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