Leticia Hernando
Universidad del País Vasco
Bilbao, Spain
Researcher
Bio
Leticia Hernando Rodriguez is a researcher at Universidad del País Vasco (UPV/EHU) with 247 citations according to her Google Scholar profile. She is actively involved in research related to evolutionary computation and combinatorial optimization, with particular expertise in permutation-based problems and probabilistic modeling.
Her research focuses on developing innovative approaches to combinatorial optimization problems, including the creation of tunable generators for permutation-based optimization instances and methods for estimating the complexity of optimization landscapes.
Research Focus
Evolutionary Computation: Her primary research focuses on evolutionary algorithms and their applications to complex combinatorial optimization problems, with extensive publications in top-tier venues like IEEE Transactions on Evolutionary Computation.
Combinatorial Optimization: She specializes in permutation-based combinatorial optimization problems, developing novel probabilistic models and solution approaches for challenging problems like the Quadratic Assignment Problem and Linear Ordering Problem.
Probabilistic Models: Her work includes the development of the generalized Mallows model for permutations, which provides a principled approach to generating challenging instances of permutation-based optimization problems.
Optimization Landscape Analysis: She has contributed significantly to understanding the structure of optimization problems through methods for estimating the number of local optima in combinatorial optimization landscapes.
Key Publications
- “A tunable generator of instances of permutation-based combinatorial optimization problems” - IEEE Transactions on Evolutionary Computation, 2016
- “An evaluation of methods for estimating the number of local optima in combinatorial optimization problems” - Evolutionary Computation, 2013
- Multiple publications in GECCO (Genetic and Evolutionary Computation Conference) proceedings (2019-2023)
Research Applications
- Quadratic Assignment Problem - Advanced solution techniques and instance generation
- Linear Ordering Problem - Optimization approaches and complexity analysis
- Permutation Problems - Probabilistic modeling and algorithmic development
- Optimization Benchmarking - Development of challenging test instances for algorithm evaluation
Interests
Evolutionary Computation | Combinatorial Optimization | Probabilistic Models | Permutation Problems | Optimization Landscape Analysis
