mspMEA: The Microcones Separation Parallel Multiobjective Evolutionary Algorithm and Its Application to Fuzzy Rule-Based Ship Classification

Cococcioni, Marco

doi:10.1007/978-3-319-26450-9_17

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Finally, note how the proposed approach can be easily parallelized on modern many-cores processors, following the same approach provided in [49], or can be tailored to deal with problems with non-convex Pareto fronts, as done in [50].…”

Section: Discussionmentioning

confidence: 99%

Solving mixed Pareto-Lexicographic multi-objective optimization problems: The case of priority chains

Lai¹,

Fiaschi²,

Cococcioni³

2020

Swarm and Evolutionary Computation

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Section: Discussionmentioning

confidence: 99%

Solving mixed Pareto-Lexicographic multi-objective optimization problems: The case of priority chains

Lai¹,

Fiaschi²,

Cococcioni³

2020

Swarm and Evolutionary Computation

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“…Furthermore, the same actions used in [5][13] to speed up the fuzzy identification can be applied here. As a future work we are also considering to speed up our multiobjective evolutionary fuzzy system by using parallel MEAs (as discussed in [7]) and by moving the fitness evaluation on the GP-GPU, as done in [11]. All these actions would allow to approach bathymetry estimation even when using upcoming high-resolution, hyper-spectral images.…”

Section: Discussionmentioning

confidence: 99%

Multiobjective evolutionary optimization of quadratic Takagi-Sugeno fuzzy rules for remote bathymetry estimation

Cococcioni

Lazzerini

2015

OCEANS 2015 - Genova

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In this work we tackle the problem of bathymetry estimation using: i) a multispectral optical image of the region of interest, and ii) a set of in situ measurements. The idea is to learn the relation that between the reflectances and the depth using a supervised learning approach. In particular, quadratic Takagi-Sugeno fuzzy rules are used to model this relation. The rule base is optimized by means of a multiobjective evolutionary algorithm. To the best of our knowledge this work represents the first use of a quadratic Takagi-Sugeno fuzzy system optimized by a multiobjective evolutionary algorithm with bounded complexity, i.e., able to control the complexity of the consequent part of second-order fuzzy rules. This model has an outstanding modeling power, without inheriting the drawback of complexity due to the use of quadratic functions (which have complexity that scales quadratically with the number of inputs). This opens the way to the use of the proposed approach even for medium/high dimensional problems, like in the case of hyper-spectral images.

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Computational Intelligence in Maritime Security and Defense: Challenges and Opportunities

Cococcioni

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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mspMEA: The Microcones Separation Parallel Multiobjective Evolutionary Algorithm and Its Application to Fuzzy Rule-Based Ship Classification

Cited by 4 publications

References 19 publications

Solving mixed Pareto-Lexicographic multi-objective optimization problems: The case of priority chains

Solving mixed Pareto-Lexicographic multi-objective optimization problems: The case of priority chains

Multiobjective evolutionary optimization of quadratic Takagi-Sugeno fuzzy rules for remote bathymetry estimation

Computational Intelligence in Maritime Security and Defense: Challenges and Opportunities

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