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In this article, we explore the use of meta-heuristic algorithms for costly black-box permutation optimization problems. These combinatorial problems are defined by solution spaces that consist of permutations of elements, with an objective function that lacks a closed mathematical representation and is expensive to evaluate. The focus of our investigation is the Asteroid Routing Problem (ARP), which seeks to determine the optimal sequence of asteroids to be visited by a spacecraft while minimizing energy consumption and travel time. Specifically, we assess the performance of a simple algorithm called FAT-RLS, which primarily relies on a randomized local search approach, enhanced with a tabu structure and a mechanism to adjust the perturbation strength. We conducted a series of experiments on well-established instances of the ARP to compare the effectiveness of FAT-RLS against two recognized meta-heuristics designed for this problem, namely, UMM and CEGO. Experiments were conducted in both uninformed and informed settings, where the meta-heuristics are initialized with a specifically designed constructive algorithm for the ARP. The results demonstrate that FAT-RLS is consistently superior to UMM, while there is no conclusive evidence for the comparison with CEGO, though the FAT-RLS results seem slightly better.

The recent advancements in Artificial Intelligence have paved the way for remarkable achievements in tasks that have traditionally posed challenges even for humans [...]

L’articolo descrive la progettazione, la realizzazione e le caratteristiche di un nuovo learner corpus di italiano L2: il corpus CELI. Il corpus raccoglie, sistematicamente, le produzioni scritte di apprendenti di italiano L2 che hanno superato le prove per la certificazione linguistica CELI dell’Università per Stranieri di Perugia in relazione ai livelli B1, B2, C1 e C2. Il corpus contiene 3041 testi prodotti da altrettanti apprendenti, con una distribuzione bilanciata dei token per livello di competenza. I metadati associati a ciascun testo includono: • il genere, la data di nascita, il numero di matricola e la nazionalità dell’apprendente; • il livello di competenza, secondo il QCER, relativo alla prova superata dall’apprendente; • il punteggio totale assegnato all’intera prova d’esame, il punteggio totale assegnato alla componente scritta dell’esame, il punteggio specifico assegnato alla singola produzione scritta, con i relativi punteggi assegnati alla competenza lessicale, alla competenza grammaticale, alla competenza sociolinguistica, e alla coerenza e coesione del testo prodotto; • il numero identificativo della traccia utilizzata per la produzione di ciascun testo, con l’indicazione del genere (lettera, e-mail, blog, racconto, articolo e relazione) e della tipologia (argomentativa, descrittiva e narrativa, o mista: descrittiva-narrativa; argomentativa-narrativa; argomentativa-descrittiva; argomentativa-narrativa-descrittiva) del testo prodotto. Il corpus CELI si presta a numerosi utilizzi sia sul versante della ricerca linguistico-acquisizionale, soprattutto in chiave pseudo-longitudinale, sia sul versante della pianificazione didattica, dello sviluppo di attività didattiche e del language testing. The CELI corpus: a new resource for studying Italian L2 acquisition The article illustrates the design, the development and the characteristics of a new learner corpus of Italian L2: the CELI corpus. The corpus systematically collects the written texts produced by learners of Italian L2 who have passed the CELI exams administered by University for Foreigners of Perugia in relation to proficiency levels B1, B2, C1 and C2. The corpus contains 3041 texts produced by the same number of learners, with a balanced distribution of the tokens in terms of proficiency level. The metadata associated with each text include: • gender, date of birth, student ID code and nationality of the learner; • CEFR proficiency level, related to the exam passed by the learner; • the score assigned to the entire exam, to the entire written component of the exam, and to the single written task together with scores pertaining to lexical, grammatical and sociolinguistic competence and to the cohesion and coherence of the produced text; • the ID number related to the task used to produce each text, with the indication of text genre (letter, e-mail, blog, story, article and report) and text type (argumentative, descriptive and narrative, or mixed: descriptive-narrative; argumentative-narrative; argumentative-descriptive; argumentative-narrative-descriptive). The CELI corpus lends itself to numerous uses both in the domain of second language acquisition research, particularly with regard to pseudo-longitudinal research designs, and in the domain of pedagogical planning, pedagogical materials design and language testing.

The future of education lies in the ability to develop technologies which integrate seamless artificial intelligence (AI) components into the educational process, in order to deliver a personalized service which is dynamically tailored to the learner’s characteristics, abilities, and needs [...]

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.

This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions.

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.