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Mathematics
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Evolutionary Algorithms in Artificial Intelligent Systems
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Evolutionary Algorithms in Artificial Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17604

Special Issue Editors

Prof. Dr. Alfredo Milani

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Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: online evolutionary algorithms; metaheuristic for combinatorial optimization; discrete differential evolution; semantic proximity measures; planning agents and complex network dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valentina Franzoni

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: artificial intelligence; emotion recognition; learner behaviour modeling; semantic proximity measures; link prediction; deep learning algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Baioletti

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: evolutionary computation; automated planning; scheduling; probabilistic logic; possibility theory; neuroevolution

Special Issue Information

Dear Colleagues,

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. Successful early applications of the evolutionary computational approach can be found in the field of numerical optimization, while they have now become pervasive in applications for planning, scheduling, transportation and logistics, vehicle routing, packing problems, etc. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, as components of intelligent systems for supporting tasks and decisions in the fields of machine vision, natural language processing, parameters optimization for neural networks (neuroevolution), and features selection in machine learning systems. Moreover, they are also applied in areas like complex networks dynamics, evolution and trend detection in social networks, emergent behavior in multiagent systems and adaptive evolutionary user interfaces, to mention a few. In these systems, the evolutionary components are integrated in the overall architecture and they provide services, e.g., pattern matching services, to the specific algorithmic solutions.

The aim of this Special Issue is to bring together recent theoretical and applicative research advancements in the area of evolutionary algorithms as components of intelligent systems, with a focus on solutions and methodologies that can be reused to solve subclasses of problems recurring in intelligent applications.

Contributions are welcome on theoretical models and applications to intelligent systems of evolutionary algorithms for, but not limited to, single-objective and multiobjective optimization, numerical continuous nonlinear optimization, combinatorial optimization, graph matching and pattern matching, agents, and automata optimization. Evolutionary paradigms to be considered, non-exhaustively, include continuous and discrete differential evolution, genetic algorithms, memetic and foraging schemes, online evolutionary algorithms, genetic programming, co-evolution mechanisms, artificial immune systems, swarm-based approaches, ant colony optimization, and, more generally, nature and bio-inspired metaheuristics.

The selection criteria will be primarily based on the formal and technical soundness, the experimental support, and the relevance of the contribution and its impact on reusability of the results for solving subgroup of problems recurring in a class of intelligent applications.

Prof. Dr. Alfredo Milani
Prof. Dr. Valentina Franzoni
Dr. Marco Baioletti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Evolutionary components in artificial intelligent systems
  • Evolutionary machine vision
  • Evolutionary approaches to combinatorial optimization
  • Formal methods in evolutionary algorithms
  • Evolutionary algorithms in machine learning
  • Evolutionary agents and planning
  • Evolutionary hardware and robotics
  • Evolutionary schemes for crowd modeling and management
  • Evolutionary approaches to social network dynamics
  • Neuroevolution and evolutionary algorithms in neural networks
  • Evolutionary strategies for cybersecurity
  • Evolutionary human–machine and machine–machine interfaces
  • Large-scale optimisation
  • Compact optimisation
  • Estimation of distribution algorithms
  • Parameters tuning
  • Evolution strategies
  • Genetic algorithm
  • Genetic programming
  • Evolutionary programming
  • Memetic algorithms
  • Memetic computing
  • Differential evolution
  • Particle swarm optimisation
  • Ant colony optimisation
  • Bacterial foraging optimisation

Published Papers (6 papers)

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Research

21 pages, 495 KiB  
Article
Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection
by Urban Škvorc, Tome Eftimov and Peter Korošec
Mathematics 2022, 10(3), 432; https://doi.org/10.3390/math10030432 - 29 Jan 2022
Cited by 11 | Viewed by 1899
Abstract
In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of [...] Read more.
In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this paper, we are interested in the problem of knowledge transfer between the artificially generated and existing handmade benchmark problems in the domain of continuous numerical optimization. That is, can an algorithm selection model trained purely on artificially generated problems correctly provide algorithm recommendations for existing handmade problems. We show that such a model produces low-quality results, and we also provide explanations about how the algorithm selection model works and show the differences between the problem data sets in order to explain the model’s performance. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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22 pages, 1183 KiB  
Article
Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model
by Ahmed A. Ewees, Mohammed A. A. Al-qaness, Laith Abualigah, Diego Oliva, Zakariya Yahya Algamal, Ahmed M. Anter, Rehab Ali Ibrahim, Rania M. Ghoniem and Mohamed Abd Elaziz
Mathematics 2021, 9(18), 2321; https://doi.org/10.3390/math9182321 - 19 Sep 2021
Cited by 56 | Viewed by 4938
Abstract
Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, [...] Read more.
Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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23 pages, 1307 KiB  
Article
A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project
by Han Wu and Junwu Wang
Mathematics 2021, 9(12), 1421; https://doi.org/10.3390/math9121421 - 19 Jun 2021
Cited by 11 | Viewed by 1801
Abstract
In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm [...] Read more.
In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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24 pages, 1795 KiB  
Article
An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE
by Kai Zhang and Yicheng Yu
Mathematics 2021, 9(5), 569; https://doi.org/10.3390/math9050569 - 07 Mar 2021
Cited by 3 | Viewed by 1960
Abstract
Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals [...] Read more.
Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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14 pages, 5646 KiB  
Article
Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico
by Nawaf N. Hamadneh, Muhammad Tahir and Waqar A. Khan
Mathematics 2021, 9(2), 180; https://doi.org/10.3390/math9020180 - 18 Jan 2021
Cited by 21 | Viewed by 3976
Abstract
The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural [...] Read more.
The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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10 pages, 322 KiB  
Article
Discovering Correlation Indices for Link Prediction Using Differential Evolution
by Giulio Biondi and Valentina Franzoni
Mathematics 2020, 8(11), 2097; https://doi.org/10.3390/math8112097 - 23 Nov 2020
Cited by 5 | Viewed by 1679
Abstract
Binary correlation indices are crucial for forecasting and modelling tasks in different areas of scientific research. The setting of sound binary correlations and similarity measures is a long and mostly empirical interactive process, in which researchers start from experimental correlations in one domain, [...] Read more.
Binary correlation indices are crucial for forecasting and modelling tasks in different areas of scientific research. The setting of sound binary correlations and similarity measures is a long and mostly empirical interactive process, in which researchers start from experimental correlations in one domain, which usually prove to be effective in other similar fields, and then progressively evaluate and modify those correlations to adapt their predictive power to the specific characteristics of the domain under examination. In the research of prediction of links on complex networks, it has been found that no single correlation index can always obtain excellent results, even in similar domains. The research of domain-specific correlation indices or the adaptation of known ones is therefore a problem of critical concern. This paper presents a solution to the problem of setting new binary correlation indices that achieve efficient performances on specific network domains. The proposed solution is based on Differential Evolution, evolving the coefficient vectors of meta-correlations, structures that describe classes of binary similarity indices and subsume the most known correlation indices for link prediction. Experiments show that the proposed evolutionary approach always results in improved performances, and in some cases significantly enhanced, compared to the best correlation indices available in the link prediction literature, effectively exploring the correlation space and exploiting its self-adaptability to the given domain to improve over generations. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Artificial Intelligent Systems)
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