Evolutionary computation is used to find optimal solutions to multi-objective, large scale, complex optimization problems
Evolutionary computation simulates evolution and it is applied to solve a variety of design innovation and sustainability problems
Evolutionary computation uses computational models of natural evolution to solve complex problems in science and engineering. The creation of sustainable systems to meet our needs without compromising future generations is a social challenge. It demands the development of new technologies and the redesign of our infrastructure, balancing environmental, economic, and societal needs. Sustainability problems are intrinsically complex, dynamic, large-scale, span several disciplines, and require multidisciplinary efforts and methods to solve them. We use the power of evolutionary computation for design innovation and optimization of solutions to complex sustainability problems.
Smart power grids, intelligent mobility and transportation systems, and intelligent water grids are three key systems to which evolutionary computation can be used to optimize their design and improve their sustainability to meet the needs of the future. Additionally, design innovation in key industries, such as automobile and space exploration, are areas in which evolutionary computation will play an important role.
In the Laboratory we learn about real-world problem-solving and optimization by computational means. This requires a clear understanding of the problem, its modeling, and the creation of a program to solve it. There is also opportunities to collaborate in joint research with industry. These activities complements the education received in the undergraduate curse and broadens the skills of engineers.
Ecuadorian Engineer. PhD from Shinshu University in 2003. Collaborates actively with in-dustry, promotes international exchange, and conducts joint research with national and international institutions.