|
|||||
View general information Description The subject within the syllabus as a whole Professional fields to which it applies Prior knowledge Learning objectives and results Content View the UOC learning resources used in the subject Guidelines on assessment at the UOC View the assessment model | |||||
This is the course plan for the first semester of the academic year 2024/2025. To check whether the course is being run this semester, go to the Virtual Campus section More UOC / The University / Programmes of study section on Campus. Once teaching starts, you'll be able to find it in the classroom. The course plan may be subject to change. | |||||
Welcome to the Metaheuristic Optimization course, which combines concepts from Artificial Intelligence, Operations Research, Computer Science, and Industrial Engineering to develop intelligent algorithms and methods capable to tackle large-scale and NP-hard combinatorial optimization problems, even in scenarios where stochastic or dynamic conditions are considered (as it frequently happens in many real-life applications). The course is based on the many years of research and transfer activities developed by the ICSO Meta team (https://icso.webs.upv.es). During these years, we have been able to develop different types of x-heuristic algorithms, including: biased-randomized heuristics, simheuristics, learnheuristics, discrete-event heuristics, and agile-optimization algorithms.
|
|||||
Due to the interdisciplinary nature of metaheuristic algorithms, and their noticeable capacities for solving optimization challenges in many application fields, this course is related to many others in the master. In particular, it is strongly related to the Simulation and Operations Research courses. | |||||
The need for optimization of processes and systems is all around us: from transportation and logistics systems, to telecommunication networks, manufacturing facilities, smart cities, or insurance policies. Hence, x-heuristic algorithms are employed in many transfer projects with industrial and business partners. In addition, it is still a young research field with an extraordinary potential for obtaining sound results and publish them in international journals. | |||||
Some analytical skills and a strong wish to learn more about optimization algorithms are required. Also, the ability to read scientific documents in English, as well as basic programming and statistical / mathematical concepts and skills. | |||||
The main goals of the course are:
After completing the course, students should be able to:
Among the master's competencies, this course will allow you to acquire the following:
|
|||||
|
|||||
Assessment at the UOC is, in general, online, structured around the continuous assessment activities, the final assessment tests and exams, and the programme's final project. Assessment activities and tests can be written texts and/or video recordings, use random questions, and synchronous or asynchronous oral tests, etc., as decided by each teaching team. The final project marks the end of the learning process and consists of an original and tutored piece of work to demonstrate that students have acquired the competencies worked on during the programme. To verify students' identity and authorship in the assessment tests, the UOC reserves the right to use identity recognition and plagiarism detection systems. For these purposes, the UOC may make video recordings or use supervision methods or techniques while students carry out any of their academic activities. The UOC may also require students to use electronic devices (microphones, webcams or other tools) or specific software during assessments. It is the student's responsibility to ensure that these devices work properly. The assessment process is based on students' individual efforts, and the assumption that the student is the author of the work submitted for academic activities and that this work is original. The UOC's website on academic integrity and plagiarism has more information on this. Submitting work that is not one's own or not original for assessment tests; copying or plagiarism; impersonation; accepting or obtaining any assignments, whether for compensation or otherwise; collaboration, cover-up or encouragement to copy; and using materials, software or devices not authorized in the course plan or instructions for the activity, including artificial intelligence and machine translation, among others, are examples of misconduct in assessments that may have serious academic and disciplinary consequences. If students are found to be engaging in any such misconduct, they may receive a Fail (D/0) for the graded activities in the course plan (including final tests) or for the final grade for the course. This could be because they have used unauthorized materials, software or devices (such as artificial intelligence when it is not permitted, social media or internet search engines) during the tests; copied fragments of text from an external source (the internet, notes, books, articles, other students' work or tests, etc.) without the corresponding citation; purchased or sold assignments, or undertaken any other form of misconduct. Likewise and in accordance with the UOC's academic regulations, misconduct during assessment may also be grounds for disciplinary proceedings and, where appropriate, the corresponding disciplinary measures, as established in the regulations governing the UOC community (Normativa de convivència). In its assessment process, the UOC reserves the right to:
Artificial intelligence in assessments The UOC understands the value and potential of artificial intelligence (AI) in education, but it also understands the risks involved if it is not used ethically, critically and responsibly. So, in each assessment activity, students will be told which AI tools and resources can be used and under what conditions. In turn, students must agree to follow the guidelines set by the UOC when it comes to completing the assessment activities and citing the tools used. Specifically, they must identify any texts or images generated by AI systems and they must not present them as their own work. In terms of using AI, or not, to complete an activity, the instructions for assessment activities indicate the restrictions on the use of these tools. Bear in mind that using them inappropriately, such as using them in activities where they are not allowed or not citing them in activities where they are, may be considered misconduct. If in doubt, we recommend getting in touch with the course instructor and asking them before you submit your work. |
|||||
|