Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms. - Adriana Lara; Carlos A. Coello Coello; Oliver Schütze 001 (IEEE Congress on Evolutionary Computation, 2009)

Evolutionary continuation methods for optimization problems. - Oliver Schütze 001; Adriana Lara; Carlos A. Coello Coello (GECCO, 2009)

A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems. - Adriana Lara Lopez; Carlos A. Coello Coello; Oliver Schütze 001 (IEEE Congress on Evolutionary Computation, 2010)

Computing approximate solutions of scalar optimization problems and applications in space mission design. - Oliver Schütze 001; Adriana Lara; Carlos A. Coello Coello; Massimiliano Vasile (IEEE Congress on Evolutionary Computation, 2010)

Using gradient information for multi-objective problems in the evolutionary context. - Adriana Lara; Carlos A. Coello Coello; Oliver Schütze 001 (GECCO (Companion), 2010)

New challenges for memetic algorithms on continuous multi-objective problems. - Adriana Lara; Oliver Schütze 001; Carlos A. Coello Coello (GECCO (Companion), 2010)

Some comments on GD and IGD and relations to the Hausdorff distance. - Oliver Schütze 001; Xavier Esquivel; Adriana Lara; Carlos A. Coello Coello (GECCO (Companion), 2010)

HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms. - Adriana Lara; Gustavo Sanchez; Carlos A. Coello Coello; Oliver Schütze 001 (IEEE Trans. Evol. Comput., 2010)

On the Influence of the Number of Objectives on the Hardness of a Multiobjective Optimization Problem. - Oliver Schütze 001; Adriana Lara; Carlos A. Coello Coello (IEEE Trans. Evol. Comput., 2011)

The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algorithms. - Adriana Lara; Sergio Alvarado; Shaul Salomon; Gideon Avigad; Carlos A. Coello Coello; Oliver Schütze 001 (EVOLVE, 2012)

Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. - Oliver Schütze 001; Xavier Esquivel; Adriana Lara; Carlos A. Coello Coello (IEEE Trans. Evol. Comput., 2012)

On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms. - Adriana Lara; Oliver Schütze 001; Carlos A. Coello Coello (EVOLVE, 2013)

The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. - Víctor Adrián Sosa-Hernández; Adriana Lara; Heike Trautmann; Günter Rudolph; Oliver Schütze 001 (NEO, 2015)

An effective mutation operator to deal with multi-objective constrained problems: SPM. - Sergio Alvarado; Adriana Lara; Víctor Adrián Sosa-Hernández; Oliver Schütze 001 (CCE, 2016)

A local exploration tool for linear many objective optimization problems. - Oliver Cuate; Adriana Lara; Oliver Schütze 001 (CCE, 2016)

The directed search method for multi-objective memetic algorithms. - Oliver Schütze 001; Adanay Martín; Adriana Lara; Sergio Alvarado; Eduardo Salinas; Carlos A. Coello Coello (Comput. Optim. Appl., 2016)

A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems. - Oliver Cuate; Lourdes Uribe; Antonin Ponsich; Adriana Lara; Fernanda Beltran; Alberto Rodríguez Sánchez; Oliver Schütze 001 (EMO, 2019)

Toward a New Family of Hybrid Evolutionary Algorithms. - Lourdes Uribe; Oliver Schütze 001; Adriana Lara (EMO, 2019)

On the choice of neighborhood sampling to build effective search operators for constrained MOPs. - Adriana Lara; Lourdes Uribe; Sergio Alvarado; Víctor Adrián Sosa-Hernández; Honggang Wang 002; Oliver Schütze 001 (Memetic Comput., 2019)

Using gradient-free local search within MOEAs for the treatment of constrained MOPs. - Lourdes Uribe; Adriana Lara; Kalyanmoy Deb; Oliver Schütze 001 (GECCO Companion, 2020)

A benchmark for equality constrained multi-objective optimization. - Oliver Cuate; Lourdes Uribe; Adriana Lara; Oliver Schütze 001 (Swarm Evol. Comput., 2020)

On the efficient computation and use of multi-objective descent directions within constrained MOEAs. - Lourdes Uribe; Adriana Lara; Oliver Schütze 001 (Swarm Evol. Comput., 2020)

A new gradient free local search mechanism for constrained multi-objective optimization problems. - Lourdes Uribe; Adriana Lara; Kalyanmoy Deb; Oliver Schütze 001 (Swarm Evol. Comput., 2021)

Studying Special Operators for the Application of Evolutionary Algorithms in the Seek of Optimal Boolean Functions for Cryptography. - Sara Mandujano; Juan Carlos Ku-Cauich; Adriana Lara (MICAI (1), 2022)

Modelado del problema de composición musical polifónica automática como un problema de optimización biobjetivo. - Natan Vilchis; Adriana Lara (Res. Comput. Sci., 2022)

Automatic Composition of Music Using a Genetic Algorithm, Emotional Musical Theory and Machine Learning. - Adriana Lara; Giovanni Guzmán; Natan Vilchis (Computación y Sistemas (CyS), 2023)

Optimizing Affordably Tree Seeding to Enhance Mexico City's Air Quality. - Fernando Moreno; Adriana Lara (Res. Comput. Sci., 2023)

Using Evolutionary Algorithms for the Search of 16-Variable Weight-Wise Perfectly Balanced Boolean Functions with High Non-linearity. - Sara Mandujano; Adriana Lara; Juan Carlos Ku-Cauich (PPSN (4), 2024)

Routing and Scheduling in Multigraphs With Time Constraints - A Memetic Approach for Airport Ground Movement. - Lilla Beke; Lourdes Uribe; Adriana Lara; Carlos Artemio Coello Coello; Michal Weiszer; Edmund K. Burke; Jun Chen 009 (IEEE Trans. Evol. Comput., 2024)