Abstract
This overview is a recent literature on simulation-based multi-objective evolutionary algorithms (SMOEAs) capable of handling stochastic multiple objective functions. Special attention is given to stochastic multi-objective problems as well as to combinations of multi-objective evolutionary algorithms with simulation techniques. Then we illustrate the principale working of cooperation between Simulation and MOEAs, and discuss their application scope. Finally, it highlights recent important trends and closely related research fields.
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Notes
- 1.
A copula is a function which joins or couples a multivariate distribution function to its one-dimensional marginal distribution functions.
- 2.
Is a stochastic simulation used for sparse continuous data and is a Monte Carlo method for generating equiprobable realizations of a continuous property which reproduce its frequency distribution and spatial correlation function.
- 3.
Is a variogram-based categorical simulation technique and is a commonly used method for discrete variable simulation.
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Gannouni, A., Ellaia, R. (2024). An Overview of Simulation-Based Multi-objective Evolutionary Algorithms. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-54318-0_6
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