Abstract
In this paper we discuss three topics that are present in the area of real-world optimization, but are often neglected in academic research in evolutionary computation community. First, problems that are a combination of several interacting sub-problems (so-called multi-component problems) are common in many real-world applications and they deserve better attention of research community. Second, research on optimisation algorithms that focus the search on the edges of feasible regions of the search space is important as high quality solutions usually are the boundary points between feasible and infeasible parts of the search space in many real-world problems. Third, finding bottlenecks and best possible investment in real-world processes are important topics that are also of interest in real-world optimization. In this chapter we discuss application opportunities for evolutionary computation methods in these three areas.
Keywords
- Integer Linear Program
- Travel Salesman Problem
- Travel Salesman Problem
- Knapsack Problem
- Vehicle Rout Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- 1.
By real-world problems we mean problems which are found in some business/industry on daily (regular) basis. See [36] for a discussion on different interpretations of the term “real-world problems”.
- 2.
Available at: http://people.brunel.ac.uk/~mastjjb/jeb/info.html.
- 3.
The term removing a bottleneck refers to the investment in the resources related to that bottleneck to prevent those resources from constraining the problem solver to achieve better objective values.
- 4.
http://tinyurl.com/msexceldss, last accessed 29th March 2014.
- 5.
We have made several such industry-inspired stories and benchmarks available: http://cs.adelaide.edu.au/~optlog/research/bottleneck-stories.htm.
- 6.
we have excluded this topic from this chapter because of the lack of space.
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Bonyadi, M.R., Michalewicz, Z. (2016). Evolutionary Computation for Real-World Problems. In: Matwin, S., Mielniczuk, J. (eds) Challenges in Computational Statistics and Data Mining. Studies in Computational Intelligence, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-319-18781-5_1
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