Cómputo evolutivo
This course is part of Introducción a la inteligencia artificial.
This comprehensive course explores evolutionary computation (EC), a field that applies natural evolution and genetic theories to computational structures. Students will learn how evolutionary algorithms provide alternative approaches to solving complex problems across diverse fields including engineering, economics, medicine, and arts. The curriculum begins with fundamental concepts of genetic algorithms and evolutionary computation, then progresses to practical implementation and applications. You'll master genetic operators like crossover and mutation, understand the schema theorem, and learn to formulate decision variables for various problem domains. The course also covers other bio-inspired optimization techniques like particle swarm optimization and differential evolution, providing a well-rounded understanding of nature-inspired algorithms for optimization and search problems.
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What you'll learn
Understand the fundamental principles of evolutionary computation and genetic algorithms
Formulate and identify decision variables for optimization problems across various domains
Implement basic genetic algorithms with appropriate selection, crossover, and mutation operators
Analyze algorithm performance using the schema theorem
Compare different encoding strategies for genetic algorithms
Solve the Traveling Salesman Problem using evolutionary approaches
Skills you'll gain
This course includes:
1.4 Hours PreRecorded video
4 assignments
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There are 4 modules in this course
The Cómputo evolutivo (Evolutionary Computing) course provides a comprehensive introduction to evolutionary algorithms and their applications in solving complex optimization and search problems. The curriculum begins with foundational concepts of evolutionary computation, explaining how these methods are inspired by natural evolution processes including selection, crossover, and mutation. Students learn the principles of genetic algorithms, starting with theoretical foundations like the schema theorem and progressing to practical implementation details. The course covers formulation of optimization problems, selection of appropriate genetic operators and parameters, and analysis of algorithm performance. Advanced topics include specialized encodings for different problem domains, with particular focus on combinatorial optimization problems like the Traveling Salesman Problem. The final module introduces other bio-inspired techniques such as particle swarm optimization and differential evolution, providing a broader perspective on nature-inspired computing approaches.
Introducción a la computación evolutiva
Module 1 · 2 Hours to complete
Principios de operación de un algoritmo genético
Module 2 · 6 Hours to complete
Implementación de un algoritmo genético básico
Module 3 · 4 Hours to complete
Aplicaciones de algoritmos genéticos y otras técnicas evolutivas
Module 4 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Introducción a la inteligencia artificial
Instructor
Investigadora Titular B
Katya Rodríguez Vázquez is a researcher at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) at the Universidad Nacional Autónoma de México (UNAM). She holds a degree in Computer Engineering from UNAM and completed her Ph.D. at the University of Sheffield, focusing on multiobjective evolutionary algorithms in nonlinear system identification. Her research areas include evolutionary and bio-inspired algorithms, multi-criteria optimization, and parallel processing, with applications in hydraulic engineering, biology, economics, and more. Rodríguez Vázquez actively participates as a professor and tutor in the postgraduate programs in Computer Science and Systems Engineering at UNAM. She also serves as a referee for national and international journals and conferences related to her research interests. Recently, she has expanded her work into bioinformatics, organizing symposia on computational approaches to biological problems. She is a member of several prestigious academies, including the Mexican Academy of Sciences. On Coursera, she teaches courses such as "Cómputo evolutivo" and "Inteligencia artificial: proyecto final."
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4.1 course rating
21 ratings
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