A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under frequency fitness assign…
Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solution…
A variant of the compensated convexity process introduced and studied by Zhang and his colleagues is considered. It makes amenable the results and tools from convex analysis. It allows the regulari…
The evolutionary algorithm recommendation is catching increasing attention when solving practical application problems since different algorithms often perform differently on different problems. To…
We consider min-max-min optimization with smooth and strongly convex objectives. Our motivation for studying this class of problems stems from its connection to the k-center problem and the growing…
The theory of evolutionary algorithms on continuous space gravitates around the evolution strategy with one individual, adaptive mutation, and elitist selection, optimizing the symmetric, quadratic…
We consider a robust optimization problem with continuous decision-dependent uncertainty (RO-CDDU), which has two significant features: an uncertainty set linearly dependent on continuous decision …
The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical human decision-making (HDM) elements, including…
An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper- C2 objective functions is proposed and analyzed. Upper- C2 is a weakly concave property that exists…
Expensive constraint multimodal optimization problems (ECMMOPs) have such characteristics as expensive objectives and constraints, and multiple optimal modalities simultaneously, which pose severe …