An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization

Hernan Aguirre (1) , Arnaud Liefooghe (2, 3) , Sébastien Verel (4) , Kiyoshi Tanaka (1)

1 Faculty of Engineering [Nagano]
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 LISIC - Laboratoire d'Informatique Signal et Image de la Côte d'Opale

This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.

Domaines: Intelligence artificielle [cs.AI]

Hernan Aguirre, Arnaud Liefooghe, Sébastien Verel, Kiyoshi Tanaka. An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization. Parallel Problem Solving from Nature - PPSN XIII, Sep 2014, Ljubljana, Slovenia. pp.487-497. ⟨hal-01066206⟩