Abstract
Estimation of distribution algorithms have been shown to perform well on a wide variety of single–objective optimization problems. Here, we look at a simple – yet effective – extension of this paradigm for multi–objective optimization, called the naive \({\mathbb M}\)ID\({\mathbb E}\)A. The probabilistic model in this specific algorithm is a mixture distribution, and each component in the mixture is a univariate factorization. Mixture distributions allow for wide–spread exploration of the Pareto front thus aiding the important preservation of diversity in multi–objective optimization. Due to its simplicity, speed, and effectiveness the naive \({\mathbb M}\)ID\({\mathbb E}\)A can well serve as a baseline algorithm for multi–objective evolutionary algorithms.
Keywords
- Pareto Front
- Knapsack Problem
- Objective Space
- Mixture Distribution
- Pareto Optimal Front
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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Bosman, P.A.N., Thierens, D. (2005). The Naive \({\mathbb M}\)ID\({\mathbb E}\)A: A Baseline Multi–objective EA. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_30
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DOI: https://doi.org/10.1007/978-3-540-31880-4_30
Publisher Name: Springer, Berlin, Heidelberg
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