A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

[EN]This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods

Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool

[EN] Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies.Esparcia Alcázar, AI.; Almenar-Pedrós, F.; Vos, TE.; Rueda Molina, U. (2018). Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool. Memetic Computing. 10(3):257-265. https://doi.org/10.1007/s12293-018-0263-8S257265103Aho P, Menz N, Rty T (2013) Dynamic reverse engineering of GUI models for testing. In: Proceedings of 2013 international conference on control, decision and information technologies (CoDIT’13)Aho P, Oliveira R, Algroth E, Vos T (2016) Evolution of automated testing of software systems through graphical user interface. In: Procs. of the 1st international conference on advances in computation, communications and services (ACCSE 2016), Valencia, pp 16–21Alegroth E, Feldt R, Ryrholm L (2014) Visual GUI testing in practice: challenges, problems and limitations. Empir Softw Eng 20:694–744. https://doi.org/10.1007/s10664-013-9293-5Barr ET, Harman M, McMinn P, Shahbaz M, Yoo S (2015) The oracle problem in software testing: a survey. IEEE Trans Softw Eng 41(5):507–525Bauersfeld S, Vos TEJ (2012) A reinforcement learning approach to automated GUI robustness testing. In: Fast abstracts of the 4th symposium on search-based software engineering (SSBSE 2012), pp 7–12Bauersfeld S, de Rojas A, Vos T (2014) Evaluating rogue user testing in industry: an experience report. In: 2014 IEEE eighth international conference on research challenges in information science (RCIS), pp 1–10. https://doi.org/10.1109/RCIS.2014.6861051Bauersfeld S, Vos TEJ, Condori-Fernández N, Bagnato A, Brosse E (2014) Evaluating the TESTAR tool in an industrial case study. In: 2014 ACM-IEEE international symposium on empirical software engineering and measurement, ESEM 2014, Torino, Italy, September 18–19, 2014, p 4Bauersfeld S, Wappler S, Wegener J (2011) A metaheuristic approach to test sequence generation for applications with a GUI. In: Cohen MB, Ó Cinnéide M (eds) Search based software engineering: third international symposium, SSBSE 2011, Szeged, Hungary, September 10-12, 2011. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 173–187Brameier MF, Banzhaf W (2010) Linear genetic programming, 1st edn. Springer, New YorkChaudhary N, Sangwan O (2016) Metrics for event driven software. Int J Adv Comput Sci Appl 7(1):85–89Esparcia-Alcázar AI, Almenar F, Martínez M, Rueda U, Vos TE (2016) Q-learning strategies for action selection in the TESTAR automated testing tool. In: Proceedings of META 2016 6th international conference on metaheuristics and nature inspired computing, pp 174–180Esparcia-Alcázar AI, Almenar F, Rueda U, Vos TEJ (2017) Evolving rules for action selection in automated testing via genetic programming–a first approach. In: Squillero G, Sim K (eds) Applications of evolutionary computation: 20th European conference, evoapplications 2017, Amsterdam, The Netherlands, April 19–21, 2017, Proceedings, part II. Springer, pp 82–95. https://doi.org/10.1007/978-3-319-55792-2_6Esparcia-Alcázar AI, Moravec J (2013) Fitness approximation for bot evolution in genetic programming. Soft Comput 17(8):1479–1487. https://doi.org/10.1007/s00500-012-0965-7He W, Zhao R, Zhu Q (2015) Integrating evolutionary testing with reinforcement learning for automated test generation of object-oriented software. Chin J Electron 24(1):38–45Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeLehman J, Stanley KO (2011) Novelty search and the problem with objectives. In: Riolo R, Vladislavleva E, Moore JH (eds) Genetic programming theory and practice IX, genetic and evolutionary computation. Springer, New York, pp 37–56Memon AM, Soffa ML, Pollack ME (2001) Coverage criteria for GUI testing. In: Proceedings of ESEC/FSE 2001, pp 256–267Rueda U, Vos TEJ, Almenar F, Martínez MO, Esparcia-Alcázar AI (2015) TESTAR: from academic prototype towards an industry-ready tool for automated testing at the user interface level. In: Canos JH, Gonzalez Harbour M (eds) Actas de las XX Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2015), pp 236–245Seesing A, Gross HG (2006) A genetic programming approach to automated test generation for object-oriented software. Int Trans Syst Sci Appl 1(2):127–134Vos TE, Kruse PM, Condori-Fernández N, Bauersfeld S, Wegener J (2015) TESTAR: tool support for test automation at the user interface level. Int J Inf Syst Model Des 6(3):46–83. https://doi.org/10.4018/IJISMD.2015070103Wappler S, Wegener J (2006) Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO’06. ACM, New York, NY, USA, pp 1925–1932. URL https://doi.org/10.1145/1143997.1144317Watkins C (1989) Learning from delayed rewards. Ph.D. Thesis. Cambridge Universit

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Space mission risk, sustainability and supply chain: review, multi-objective optimization model and practical approach

This paper investigates the convergence of risk, sustainability, and supply chain in space missions, including a review of fundamental concepts, the introduction of a multi-objective conceptual optimization model, and the presentation of a practical approach. Risks associated with space missions include technical, human, launch, space environment, mission design, budgetary, and political risks. Sustainability considerations must be incorporated into mission planning and execution to ensure the long-term viability of space exploration. The study emphasizes the importance of considering environmental sustainability, resource use, ethical concerns, long-term planning, international collaboration, and public outreach in space missions. It emphasizes the significance of reducing negative environmental consequences, increasing resource use efficiency, and making responsible and ethical actions. The paper offers a multi-objective optimization conceptual model that may be used to evaluate and choose sustainable space mission tactics. This approach considers a variety of elements, including environmental effects, resource utilization, mission cost, and advantages for society. It provides a systematic decision-making approach that examines trade-offs between different criteria and identifies optimal conceptual model solutions that balance risk, sustainability, and supply chain objectives. A practical approach is also offered to demonstrate the use of the multi-criteria optimization conceptual model in a space mission scenario. The practical approach demonstrates how the model can aid in the development of mission strategies that minimize risks, maximize resource consumption, and fit with sustainability goals. Overall, this paper delivers a multi-criteria optimization conceptual model and provides a space mission planning practical approach, as well as an overview of the interaction between risk, sustainability, and supply chain in space mission organization, planning, and execution.This research was partially supported by the AGH University of Science and Technology, Kraków, Poland (16.16.200.396) and the financial aid of the Polish Ministry of Science and Higher Education (MNISW) grants (N N519 405934; 6459/B/T02/2011/40) and the Polish National Science Centre (NCN) research grant (DEC-2013/11/B/ST8/04458). Moreover, I appreciate the support of the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033). Additionally, I acknowledge the support from the Public University of Navarra, Pamplona, Spain and the University of California at Berkeley, USA. The research was also partially supported by the European Union Horizon 2020 research and innovation program under Marie-Skłodowska Curie, No: 101034285

Applications, Operational Architectures and Development of Virtual Power Plants as a Strategy to Facilitate the Integration of Distributed Energy Resources

In this article, we focus on the development and scope of virtual power plants (VPPs) as a strategy to facilitate the integration of distributed energy resources (DERs) in the power system. Firstly, the concepts about VPPs and their scope and limitations are introduced. Secondly, smart management systems for the integration of DERs are considered and a scheme of DER management through a bottom-up strategy is proposed. Then, we analyze the coordination of VPPs with the system operators and their commercial integration in the electricity markets. Finally, the challenges that must be overcome to achieve the large-scale implementation of VPPs in the power system are identified and discussed.The authors acknowledge the support from GISEL research group IT1191-19, as well as from the University of the Basque Country UPV/EHU (research group funding 181/18)

Adjustable Robust Reinforcement Learning for Online 3D Bin Packing

Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints. While current deep reinforcement learning (DRL) methods for online 3D-BPP have shown promising results in optimizing average performance over an underlying box sequence distribution, they often fail in real-world settings where some worst-case scenarios can materialize. Standard robust DRL algorithms tend to overly prioritize optimizing the worst-case performance at the expense of performance under normal problem instance distribution. To address these issues, we first introduce a permutation-based attacker to investigate the practical robustness of both DRL-based and heuristic methods proposed for solving online 3D-BPP. Then, we propose an adjustable robust reinforcement learning (AR2L) framework that allows efficient adjustment of robustness weights to achieve the desired balance of the policy's performance in average and worst-case environments. Specifically, we formulate the objective function as a weighted sum of expected and worst-case returns, and derive the lower performance bound by relating to the return under a mixture dynamics. To realize this lower bound, we adopt an iterative procedure that searches for the associated mixture dynamics and improves the corresponding policy. We integrate this procedure into two popular robust adversarial algorithms to develop the exact and approximate AR2L algorithms. Experiments demonstrate that AR2L is versatile in the sense that it improves policy robustness while maintaining an acceptable level of performance for the nominal case.Comment: Accepted to NeurIPS202