Improving Maritime Awareness with Semantic Genetic Programming and Linear Scaling: Prediction of Vessels Position Based on AIS Data

Vanneschi, Leonardo; Castelli, Mauro; Costa, Ernesto; Re, Alessandro; Vaz, Henrique; Lobo, Victor; Urbano, Paulo

doi:10.1007/978-3-319-16549-3_59

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Even though LS has been applied to GP several times, if we exclude [14], so far in the literature it is possible to find only one contribution in which LS has been integrated with GSGP: in 2015, Vanneschi et al [26] applied LS to GSGP for tackling an application in the maritime awareness domain. The objective of that work was to predict the position of vessels at sea, based on information related to the vessels' past positions in a specific time interval, using AIS data.…”

Section: Previous and Related Workmentioning

confidence: 99%

Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution

Nadizar,

Sakallioglu,

Garrow

et al. 2024

Genet Program Evolvable Mach

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Geometric Semantic Genetic Programming (GSGP) has shown notable success in symbolic regression with the introduction of Linear Scaling (LS). This achievement stems from the synergy of the geometric semantic genetic operators of GSGP with the scaling of the individuals for computing their fitness, which favours programs with a promising behaviour. However, the initial combination of GSGP and LS (GSGP-LS) underutilised the potential of LS, scaling individuals only for fitness evaluation, neglecting to incorporate improvements into their genetic material. In this paper we propose an advancement, GSGP with Lamarckian LS (GSGP-LLS), wherein we update the individuals in the population with their scaling coefficients in a Lamarckian fashion, i.e., by inheritance of acquired traits. We assess GSGP-LS and GSGP-LLS against standard GSGP for the task of symbolic regression on five hand-tailored benchmarks and six real-life problems. On the former ones, GSGP-LS and GSGP-LLS both consistently improve GSGP, though with no clear global superiority between them. On the real-world problems, instead, GSGP-LLS steadily outperforms GSGP-LS, achieving faster convergence and superior final performance. Notably, even in cases where LS induces overfitting on challenging problems, GSGP-LLS surpasses GSGP-LS, due to its slower and more localised optimisation steps.

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Section: Previous and Related Workmentioning

confidence: 99%

Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution

Nadizar,

Sakallioglu,

Garrow

et al. 2024

Genet Program Evolvable Mach

Self Cite

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“…There are a few studies, which were performed by using WEKA. For instance, Vanneschi et al (2015) used WEKA software to estimate the position of vessels, based on information related to the vessel's past positions in a specific time interval. Analysis of marine traffic flow characteristics was determined based on WEKA data mining (Zheng et al, 2009).…”

Section: The Weka Softwarementioning

confidence: 99%

Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports

Maya

Arslan

Akyüz

et al. 2020

Ships and Offshore Structures

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The application of data mining techniques is an extended practice in numerous domains; however, within the context of maritime inspections, the aforementioned methods are rarely applied. Thus, the application of datamining techniques for the prediction and ranking of non-conformities identified during vessel inspections could be of significant managerial contribution to the safety of shipping companies, as non-conformities could potentially lead to real accidents if not addressed adequately. Hence, specific data mining methods are investigated and applied in this paper to predict and rank non-conformities on oil tankers using a database recorded by tanker shipping companies in Turkey from 2006 to 2019. The results of this study reveal that specific non-conformities, for instance, inadequate ice operations or inadequate general appearance and condition of hull, superstructure and external weather decks, are not company-based problems, rather they are industry wide issues for all tanker shipping companies.

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“…In particular, GSGP is characterized by a better generalization ability. Despite its ability in outperforming traditional syntax-based genetic operators over several domains and its theoretical properties [37,38,39,40], GSGP is characterized by a rapid growth of the individuals when crossover and mutation are applied [30]. This is an important issue because it prevents GP practitioners to understand the final model produced by GSGP.…”

Section: Genetic Programming For Protein Foldingmentioning

confidence: 99%

Computational Intelligence for Life Sciences

Besozzi

Manzoni

Nobile

et al. 2019

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Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing Preprint versionturn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.

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Improving Maritime Awareness with Semantic Genetic Programming and Linear Scaling: Prediction of Vessels Position Based on AIS Data

Cited by 9 publications

References 22 publications

Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution

Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution

Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports

Computational Intelligence for Life Sciences

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