Editorial: Special issue ISA 2023

Funding for open access charge: Universidade da Coruña/CISUG.[Abstract] ISA 2023 is a significant forum for presenting the development and applications of innovative techniques in closely related areas. The exchange of ideas between scientists and technicians from both academic and business sectors is essential to facilitate the development of systems that meet the demands of today’s society. Technology transfer in this field remains a challenge, so such contributions are mainly considered in this symposium. The ISA Special Session features discussions and publications on developing innovative techniques for complex problems. This Special Issue includes 11 papers selected from extended contributions presented at the Special Session on Intelligent Systems Applications (ISA) under the framework of the 20th International Symposium on Distributed Computing and Artificial Intelligence 2023 (DCAI 2023), held in Guimaraes, Portugal, 12–14 July 2023, and organized by LASI and Centro Algoritmi of the University of Minho (Portugal)

Trading rule discovery on Warsaw Stock Exchange using coevolutionary algorithms

This paper presents an application of coevolutionary algorithms to rule discovery on stock market. We used\ud genetic programming techniques with coevolution in financial\ud data mining process. There were tested a various approaches\ud to include coevolution aspects in task of build trading rule\ud (buy and sell decision). Trading rules are based on technical\ud and fundamental indicators included in decision tree and were\ud tested on Warsaw Stock Exchange historical data

Efficiency of Specialized Genetic Operators in Non-dominated Tournament Genetic Algorithm (NTGA2) Applied to Multi-objective Multi-skill Resource Constrained Project Scheduling Problem

This proceeding paper is an accepted manuscript version accepted for publication, and is subject to Springer Nature’s AM terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms. Version of Record: Antkiewicz, M., Myszkowski, P.B., Gmyrek, K., Krzeminski, A., Calvo-Rolle, J.L. (2024). Efficiency of Specialized Genetic Operators in Non-dominated Tournament Genetic Algorithm (NTGA2) Applied to Multi-objective Multi-skill Resource Constrained Project Scheduling Problem. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2166. Springer, Cham. https://doi.org/10.1007/978-3-031-70259-4_816th International Conference on Computational Collective Intelligence, 9-11 September 2024, Leipzig, Germany.[Abstract] The Multi-Objective Multi-Skill Resource Constrained Project Scheduling Problem (MS-RCPSP) is an NP-hard real-world problem that can be solved by metaheuristics like the Non-Dominated Tournament Genetic Algorithm (NTGA2). NTGA2 method is effective as a generic black-box metaheuristic. In the paper, we present experiments to examine how effective NTGA2 is in multi-objective optimization when the black-box rule is omitted, and specialized operators are used: Cheaper Resource Crossover, Less Assignment Crossover, and Resource-Leveling Mutation. Experimental results show that specialized operators have extra computational costs, but finally, the NTGA2 method is the most effective. Results are based on the benchmark iMOPSE library, compared to state-of-the-art methods, and statistically verified