Foreword: Some advances in Immune Computation and applications

Swarm and Evolutionary Computation 50 (2019) 100596 Contents lists available at ScienceDirect Swarm and Evolutionary Computation journal homepage: www.elsevier.com/locate/swevo Foreword: Some advances in Immune Computation and applications☆ Immune computation is a relatively new field in computational intelligence when compared to artificial neural networks, evolutionary computation and fuzzy systems. Inspired by the biological immune system, Immune Computation (also known as “Artificial Immune Systems”) has been studied for many years and has attracted increasing interest among researchers. Ref. [1] by Farmer might be the first paper discussing the machine learning and adaptation principles in a biological immune system. The book edited by Dasgupta [2], which was published in 1999, is an earlier book focused on artificial immune systems. Nowadays, many models/algorithms of Artificial Immune Systems (AISs) have been proposed, which could be roughly classified into six categories: Negative Selection Algorithms [3], Immune Network Algorithms [4], Clonal Selection Algorithms [5], Dendritic Cell Algorithms [6], Negative Databases [7] and Negative Surveys [8]. Although there are many models/algorithms of AISs and they have been applied to a wide variety of applications, more efficient and practical artificial immune algorithms have been proposed in recent years [9, 10]. The application areas of AISs have also been extended. The aim of this special issue is precisely to present the state-of-the-art research developments on AISs. In this special issue, the accepted papers can be classified into three classes as follows: 1. Immune Dynamic optimization: Many real-world optimization problems are dynamic. It is a challenge to solve these problems with traditional AISs. Over the years, some studies have focused on applying immune algorithms to dynamic optimization problems. In Ref. [11], Zhang et al. embedded a learning strategy into the clustering based clonal selection algorithm. In the proposed algorithm, the individual in the cluster obtains the information from both the current cluster and the other clusters. Memory is also adopted in this case. In Ref. [12], Luo et al. studied dynamic multimodal optimization problems. These problems not only change over time, but also have multiple global optima at each time slide. To deal with such problems, the authors designed an improved version of the clonal selection algorithm, where the mutation step sizes are related to the range of the sub-populations. 2. Immune Multiobjective optimization: Multiobjective optimization problems are those having two or objectives that we aim to optimize at the same time. To deal with multiobjective optimization problems, in Ref. [13], Lin et al. proposed a multiobjective immune algorithm, where the population size is adjusted by the external archive. Furthermore, in Ref. [14], Carvalho et al. combined the ☆ non-dominated sorting genetic algorithm II (NSGA-II) with the clonal selection algorithm to find an optimal tree corresponding to a problem from the network design field. 3. Real-world Applications: In addition to the above scenarios, this special issue also contains papers focused on some real-world applications. Mnif et al. [15] adopted the immune network to develop public transportation control systems. In Ref. [16], Shang et al. considered that only one objective is not enough to obtain a better and diverse clustering performance, so they transformed clustering problems into multiobjective optimization problems and proposed a multiobjective immune algorithm based on the traditional fuzzy c-means algorithm to solve them. In Ref. [17], Dagdia designed a distributed version of the dendritic cell algorithm to cope with large-scale data classification. Something remarkable is that this special issue contains two papers in which AISs are applied to music generation. In Ref. [18], Caetano et al. modified opt-aiNet to find the combined musical instrument sounds which are similar to some reference sounds. In Ref. [19], Navarro et al. proposed a system called ChordAIS based on opt-aiNet to generate new chords interactively with the users. In this special issue, there are neither papers that model the biological immune system nor any papers that apply AISs to security and privacy problems. These two topics are, indeed, of great relevance. The modeling of biological immune systems is very important to promote the development of better and more accurate computational models of the immune system. On the other hand, the fact that our biological immune system is a self-protection system of the body, the application of AISs in security and privacy problems seems as a natural choice and we certainly expect more research in this direction in the next few years. Overall, the special issue received a total of 18 submissions, from which 9 papers were selected after a very thorough peer-review process. The guest editors would like to take this opportunity to thank all authors for their contributions and all reviewers for their hard and valuable work in reviewing the manuscripts. Last but not least, we thank the Editors-InChief of Swarm and Evolutionary Computation: Professor P. N. Suganthan and Professor Swagatam Das for their constant support and assistance during the edition of this special issue. References [1] J.D. Farmer, N.H. Packard, A.S. Perelson, The immune system, adaptation, and machine learning, Phys. D Nonlinear Phenom. 22 (1–3) (1986) 187–204. This work is partially supported by the National Natural Science Foundation of China (No. 61573327). https://doi.org/10.1016/j.swevo.2019.100596 2210-6502/© 2019 Published by Elsevier B.V. W. Luo et al. Swarm and Evolutionary Computation 50 (2019) 100596 [2] D. Dasgupta, Artificial Immune Systems and Their Applications, Springer Science & Business Media, 1999. [3] S. Forrest, A.S. Perelson, L. Allen, R. Cherukuri, Self-nonself discrimination in a computer, in: Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, IEEE, 1994, pp. 202–212. [4] J. Timmis, M. Neal, J. Hunt, An artificial immune system for data analysis, Biosystems 55 (1–3) (2000) 143–150. [5] L.N. De Castro, F.J. Von Zuben, Learning and optimization using the clonal selection principle, IEEE Trans. Evol. Comput. 6 (3) (2002) 239–251. [6] F. Gu, J. Greensmith, U. Aickelin, Theoretical formulation and analysis of the deterministic dendritic cell algorithm, Biosystems 111 (2) (2013) 127–135. [7] F. Esponda, Everything that is not important: negative databases [research frontier], IEEE Comput. Intell. Mag. 3 (2) (2008) 60–63. [8] F. Esponda, V.M. Guerrero, Surveys with negative questions for sensitive items, Stat. Probab. Lett. 79 (24) (2009) 2456–2461. [9] W. Luo, X. Lin, Recent advances in clonal selection algorithms and applications, in: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2017, pp. 1–8. [10] W. Luo, R. Liu, H. Jiang, D. Zhao, L. Wu, Three branches of negative representation of information: a survey, IEEE Trans. Emerg. Top. Comput. Intell. 2 (6) (2018) 411–425. [11] W. Zhang, W. Zhang, G.G. Yen, H. Jing, A cluster-based clonal selection algorithm for optimization in dynamic environment, Swarm Evolut. Comput. (2018). [12] W. Luo, X. Lin, T. Zhu, P. Xu, A clonal selection algorithm for dynamic multimodal function optimization, Swarm Evolut. Comput. (2018). [13] Q. Lin, Q. Zhu, N. Wang, P. Huang, W. Wang, J. Chen, Z. Ming, A multi-objective immune algorithm with dynamic population strategy, Swarm Evolut. Comput. (2018). [14] I.A. Carvalho, M.A. Ribeiro, A node-depth phylogenetic-based artificial immune system for multi-objective network design problems, Swarm Evolut. Comput. (2019). [15] S. Mnif, S. Elkosantini, S. Darmoul, L.B. Said, An immune network based distributed architecture to control public bus transportation systems, Swarm Evolut. Comput. (2018). [16] R. Shang, W. Zhang, F. Li, L. Jiao, R. Stolkin, Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels, Swarm Evolut. Comput. (2019). [17] Z.C. Dagdia, A scalable and distributed dendritic cell algorithm for big data classification, Swarm Evolut. Comput. (2018). [18] M. Caetano, A. Zacharakis, I. Barbancho, L.J. Tard on, Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multimodal optimization, Swarm Evolut. Comput. (2019). [19] M. Navarro, M.F. Caetano, G. Bernardes, L.d. Castro, Chordais: an assistive system for the generation of chord progressions with an artificial immune system, Swarm Evolut. Comput. (2019). Wenjian Luo* Anhui Province Key Laboratory of Software Engineering in Computing and Communication, School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China Mario Pavone Department of Mathematics and Computer Science, University of Catania, Catania, Italy E-mail address: [email protected]. Carlos Artemio Coello Coello CINVESTAV-IPN, Department of Computer Science, Mexico City, Mexico E-mail address: [email protected]. Licheng Jiao School of Artificial Intelligence, Xidian University, Xi'an, China E-mail address: [email protected]. Ramit Mehr The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel E-mail address: [email protected]. * Corresponding author. E-mail address: [email protected] (W. Luo). 2