We propose a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NS GA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points.Expand

In optimization studies including multi-objective optimization, the main focus is usually placed in finding the global optimum or global Pareto-optimal frontier, representing the best possible objective values.Expand

We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-focused recombination operators (UNDX and SPX).Expand

We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching.Expand

The paper follows the line of the design and evaluation of new evolutionary algorithms for constrained multi-objective optimization with a constraint handling technique and a diversity mechanism to obtain multiple nondominated solutions through the simple run of the algorithm.Expand

We introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables.Expand