| Description |
Natural Computing is an interdisciplinary field that draws inspiration from processes observed in nature to develop computational methods for solving complex problems. Among these methods, Evolutionary Algorithms have become one of the most successful and widely applied families of optimization and search techniques in acience, engineering, artificial intelligence, and data-driven research. Inspired by the principles of natural evolution, these algorithms provide powerful tools for addressing problems that are difficult or impossible to solve using traditional analytical or deterministic approaches.
This course introduces the fundamental concepts, principles and applications of Natural Computing, with a particular emphasis on Evolutionary Algorithms. Participants will learn how evolutionary search works, how optimization problems can be formulated, and how different evolutionary paradigms can be designed and adapted to real-world research problems. The course combines theoretical foundations with practical examples and hands-on exercises, illustrating the implementation and application of evolutionary methods to a variety of optimization and machine learning tasks. Current developments and research directions in Evolutionary Computation will also be discussed.
The course presents some programming-related aspects and practical implementations of Evolutionary Algorithms, but it is not intended as a general introduction to programming, artificial intelligence, or optimization theory.
Learning objectives:
By the end of the course, the participants are expected to:
understand the main concepts and foundations of Natural Computing and Evolutionary Computation formulate optimization problems suitable for evolutionary approaches understand and implement the main components of Evolutionary Algorithms, including representation, selection, variation operators, and replacement strategies evaluate and compare the performance of evolutionary methods using appropriate experimental methodologies apply Genetic Algorithms and related evolutionary techniques to research problems understand the strengths, limitations, and practical considerations associated with evolutionary optimization critically read and interpret scientific literature in Evolutionary Computation identify emerging research directions and applications of Natural Computing
Previous knowledge/skills:
The course assumes a basic understanding of mathematics at undergraduate level and general familiarity with scientific computing concepts. Previous experience with programming is desirable but not mandatory. No prior knowledge of Natural Computing, Evolutionary Algorithms, or optimization methods is required.
Technical requirements:
Participants may use the computers provided by the University or bring their own laptop.
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| Expenses |
Travel
PhD students of the CUSO DPEE are eligible for the reimbursement of incurred travel expenses by train (half-fare card, 2nd class). Claims can be done online via MyCUSO when the activity is over.
Accommodation
If you live > ~1h15 from the course place and would like to claim accommodation costs, please contact us ASAP (before the course starts)
Registration fees
CUSO members: Free
Others: contact ecologie-evolution(a)cuso.ch
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| Registration |
Deadline for registration: 31.10.2026
CUSO members: FREE
Others (MSc and postdocs): Please contact the CUSO coordinator here before registering: ecologie-evolution(at)cuso.ch
Cancellation Policy
In case of cancellations, before the 1st deadline (30.10.2026): free
Late cancellations (after 30.10.2026) or no-show: 100 CHF administrative fee
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Workshop language is English 
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