Abstract
Biological and artificial evolution can be speeded up by environmental changes. From the evolutionary computation perspective, environmental changes during the optimization process generate dynamic optimization problems (DOPs). However, only DOPs caused by intrinsic changes have been investigated in the area of evolutionary dynamic optimization (EDO). This paper is devoted to investigate artificially induced DOPs. A framework to generate artificially induced DOPs from any pseudo-Boolean problem is proposed. We use this framework to induce six different types of changes in a 0–1 knapsack problem and test which one results in higher speed up. Two strategies based on immigrants, which are used in EDO, are adapted to the artificially induced DOPs investigated here. Some types of changes did not result in better performance, while some types led to higher speed up. The algorithm with memory based immigrants presented very good performance.
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Notes
- 1.
The idea of changing the static fitness landscape in order to make the optimization process easier is also present in other approaches. For example, in multi-objectivization, a single-objective problem is transformed into a multi-objective problem [7]. Another example is adding noise to the fitness function [5].
- 2.
In a pseudo-Boolean optimization problem P, the fitness function is \(f_P(\mathbf {x}) \in \mathbb {R}\), where \(\mathbf {x}\in \mathbb {B}^l\) is a candidate solution vector with dimension l.
- 3.
The best percentages of successful runs for DOP Type 2.2 were 66% (\(l=300\)) and 42% (\(l=400\)), against 28% (\(l=300\)) and 18% (\(l=400\)) for the static environments.
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Acknowledgments
This work was funded partially by FAPESP under grant 2015/06462-1 and CNPq in Brazil, and partially by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under grant EP/K001310/1.
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Tinós, R., Yang, S. (2016). Artificially Inducing Environmental Changes in Evolutionary Dynamic Optimization. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_21
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