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They frequently work as members of large teams or as leaders of smaller teams that are responsible for determining customer needs, synthesizing alternative solutions, and selecting optimal solutions within economic, social, and resource constraints. Their wide-ranging role involves creating products and systems that meet the immediate needs of customers, and which can efficiently evolve over time to satisfy needs that are unknown at the start of the product.
Monmouth is one of very few universities in the nation offering an undergraduate program leading to a Bachelor of Science in Software Engineering degree. It uniquely combines the study of mathematics, physical science, and computer science with engineering and the humanities to produce a well-rounded, practical education.
Our BSSE program is rigorous, comprehensive, and unlike engineering programs in many large universities, it is taught entirely by a faculty of engineering scholars without the participation of teaching assistants in the classroom or laboratories. Introductory courses are also taught in small classes. Our students never participate in lectures with several hundred attendees as they do in many large universities. In the evolving world of software development, career advancement requires a well-rounded preparation in the basic principals of engineering and computer science, as well as the ability to work effectively as a member of a large team or as a leader of a smaller team.
But, given the close interrelationship of computing and social needs, a broader perspective is necessary.
Our BSSE program also provides a strong background in liberal arts, culture, and the humanities; thus enriching the imagination, understanding, and the analytic skills of our engineering degree candidates. A problem-solving approach and the need for engineered solutions lies at the very core of software engineering. Software engineers develop a deep understanding of business needs, synthesize a variety of potential solutions, evaluate those alternatives, and implement the best alternative.
The software engineer who addresses these problems needs a disciplined and analytic mind, and a laser-sharp focus on the challenges and constraints of real-world computer applications. Our BSSE program teaches a disciplined engineering approach to solving problems, while simultaneously encouraging the development of intuitive capabilities, to make technological leaps in the synthesis of alternatives. Never before has the demand been greater for highly skilled software professionals.
This is the case in companies of every size, description, and location, from automobile manufacturers, to telecommunications providers, to financial institutions, e-commerce, and those providing news and entertainment programming. It is of course necessary to be precise about what it is we are trying to measure or detect, as to avoid disagreements resulting from ambiguity in terms being used. It may be difficult to formally specify what makes a piece of software nontrivial, but intuitively attractive measure of length of the program expressed as the number of lines of code is not a sufficient indicator of complexity, as it could have an extremely low Kolmogorov 50 complexity.
Inspired by the Turing test, 51 , 52 which is based on inability to distinguish output from a person and a computer, we propose defining nontrivial software as such which would take an average experienced human programmer at least a full hour of effort to produce if given the same problem to solve. Regarding the other 2 conditions, they are much easier to specify. Reading early claims about capabilities of EA feels just like reading early predictions from artificial intelligence AI literature.
However, just like with early AI, the claims are inflated, unsupported, overly optimistic, phrased in deceptive and metaphoric language, and the solutions do not scale to the real-world problems. Here is how Koza presents the state of the field in We will show, via examples, that the recently developed genetic programming paradigm provides a way to search the space of all possible programs to find a function which solves, or approximately solves, a problem. An additional order-of-magnitude increase was achieved by making extraordinarily long runs on the largest machine the 1,node machine.
The length of the run that produced the two patentable inventions was One quickly realizes that most improvements in the field simply come from using more compute to search progressively larger parts of the solutions space, a result similar to the one expected for random search algorithm.
Here is an example of overhyped and ambiguous reporting of results, from recent work on EA. Perhaps, it is unfair to pick on this particular article, which is only available as an unreviewed preprint, but we selected it because it is highly representative of the type of work frequently published in GP, and its extremeness makes problems clear to identify. We are not alone in our skepticism; many others have arrived at exactly the same conclusions:. We examine what has been achieved in the literature, and find a worrying trend that largely small toy-problems been attempted which require only a few line of code to solve by hand.
Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished. A full understanding of open-ended evolutionary dynamics remains elusive.
There are many problems that traditional Genetic Programming GP cannot solve, due to the theoretical limitations of its paradigm. A Turing machine TM is a theoretical abstraction that express the extent of the computational power of algorithms. Any system that is Turing complete is sufficiently powerful to recognize all possible algorithms. GP is not Turing complete. Even a survey of GP community itself produced the following feedback regarding current problems being worked on:. In practice, GPs are used in the same way as GAs, for optimization of solutions to particular problems or for function optimization 37 , 38 , 55 — 58 or for software improvement.
Simulations of evolution will produce statistically similar results at least with respect to complexity of artifacts produced.
False as of If running EAs for as long as possible continued to produce nontrivial outputs, scientists would run them forever. Representative simulations of evolution will not produce similar results to those observed in nature. True as of Researchers will not be able to evolve software or other complex or novel artifacts.
There will not be any projects running EAs long-term because their outputs would quickly stop improving and stabilize. With respect to the public and general cultural knowledge, it would be reasonable to predict that educated people would know the longest-running EA, and the most complex evolved algorithm. Looking at outcomes from the made predictions, we observe that all predictions are false as of and all counterfactuals are true as of the same year as long as we look only at nontrivial products of evolutionary computations.
We are not evolving complex artifacts, we are not running EAs for as long as possible, we are not evolving software, and the public is unaware of most complex products of evolutionary computation. We tried to be as comprehensive as possible in this section, but it is possible that we have not considered some plausible explanations:. Incompetent programmers—It is theoretically possible, but is highly unlikely, that out of thousands of scientists working on evolutionary computation, all failed to correctly implement the Darwinian algorithm.
Nonrepresentative algorithms—Some 55 have suggested that EAs do not accurately capture the theory of evolution, but of course that would imply that the theory itself is not specified in sufficient detail to make falsifiable predictions. If, however, such more detailed specifications are available to GP believers, it is up to them to implement them as computer simulations for testing purposes, but no successful examples of such work are known and the known ones have not been successful in evolving software.
Inadequate fitness functions—Fitness function for a complex software product is difficult to outline and specify and may be as complex or even more complex as the software we want to evolve as it has to consider all the possible use cases and pass all unit tests. This may be the Achilles heel of GP, but it is also an objection to feasibility of programming in general and GP in particular, as both have to convert software specification into the source code.
The classical systems engineering approach is tailored to and works best in situations in which all relevant systems engineering factors are largely under the control of or can at least be well-understood and accommodated by the systems engineering organization or the program manager. As part of this effort, the software engineering degree is evaluated relative to the following Objectives and Outcomes. Likewise, we cannot rely on automated software verification as it is still an unsolved problem 5 in the general case. It is of course necessary to be precise about what it is we are trying to measure or detect, as to avoid disagreements resulting from ambiguity in terms being used. Schmidt, M. Computer algebra; parallel computations; algebraic algorithms; scientific computing.
If human programmers and biological evolution succeed with such constraints, so should Darwinian simulations. The Halting problem—Turing proved 60 that it is impossible to determine whether an arbitrary program halts, but this is also a problem for human programmers and could be easily addressed by placing time limits on considered solutions.
Program correctness—If we require evolved software to be provably correct, this would present a problem as GP does not verify produced designs but only tests them against specific unit tests. Likewise, we cannot rely on automated software verification as it is still an unsolved problem 5 in the general case. This is not really a problem as most of the human-written software is never proven to be correct and only a small portion of software engineering process relies of formal specification and Test Driven Development.
Inappropriate solutions—Literature on EA is full of examples 61 of surprising creativity of Darwinian algorithm resulting in solutions which match the letter of design specifications but not the spirit. This is similar to human-produced software and numerous examples of ways in which such software fails the goals of the initial design. Insufficient complexity of the environment not enough data, poor fitness functions —It is possible that the simulated environment is not complex enough to generate high complexity outputs in evolutionary simulations.
This does not seem correct as Internet presents a highly complex landscape in which many self-modifying computer viruses roam. A skeptic might insist that an abstract environment would be inadequate for the evolution. In the limiting case, if complete microphysical accuracy were insisted upon, the computational requirements would balloon to utterly infeasible proportions.
Requiring more realistic environmental conditions may result in an increase in necessary computational resources, a problem addressed in the next bullet. Insufficient resources compute, memory —From the history of computer science, we know of many situations speech recognition, NN training , where we had a correct algorithm but insufficient computational resources to run it to success. It is possible that we simply do not have hardware powerful enough to emulate evolution.
Software design is not amenable to evolutionary methods—Space of software designs may be discreet with no continues path via incremental fitness to the desired solutions. This is possible, but this implies that original goals of GP are unattainable and misguided. In addition, because a clear mapping exists between solutions to problems and animals as solutions to environmental problems, this would also imply that current explanation for the origin of the species is incorrect.
Darwinian algorithm is incomplete or wrong—Finally, we have to consider the possibility that the inspiration behind evolutionary computation, the Darwinian algorithm itself is wrong or at least partially incomplete. If that was true, computer simulations of such algorithm would fail to produce results comparable with observations we see in nature and a search for an alternative algorithm would need to take place. This would be an extraordinary claim and would require that we discard all the other possible explanations from this list. Perhaps, we can learn some answers from similar historical conundrums.
Why does deep learning work now, but not 20 years ago, even though many of the core ideas were there? In one sentence: We have more data, more compute, better software engineering, and a few algorithmic innovations. Consequently, the next section looks at this very likely explanation in detail. Given the timescales involved, it is impossible to replicate full-scale evolution in experimental settings, but it may be possible to do so in computer simulations, by generating new offspring in matter of milliseconds and by greatly expediting necessary fitness evaluation time, potentially reducing a multibillion year natural process to just a few years of simulation on a powerful supercomputer.
Others have thought about the same:. What algorithm could create all this in just 10 12 steps? The number 10 12 —one trillion—comes up because this is believed to be the number of generations since the dawn of life 3. Similar attempts have been made by others, for example, Shulman and Bostrom wanted to figure out computational requirements necessary to evolve AI:.
The argument from evolutionary algorithms then needs one additional premise to deliver the conclusion that engineers will soon be able to create machine intelligence, namely that we will soon have computing power sufficient to recapitulate the relevant evolutionary processes that produced human intelligence. Whether this is plausible depends both on what advances one might expect in computing technology over the next decades and on how much computing power would be required to run genetic algorithms with the same optimization power as the evolutionary process of natural selection that lies in our past.