We have recently proposed a framework of in vivo computation (IVC) which transforms the early tumor sensing problem into a computational problem. In the framework, a tumor-triggered biological grad…
The objective reduction has been regarded as a basic issue in many-objective optimization. Existing objective reduction methods identify one set of essential objectives using an approximate nondomi…
Many state-of-the-art evolutionary algorithms (EAs) can be categorized as sequential hybrid EAs, in which various EAs are sequentially executed. The timing to switch from one EA to another is criti…
Many practical multiobjective optimization problems have a nested bilevel structure in variables, which can be modeled as bilevel multiobjective optimization problems (BLMOPs). In this article, a c…
The implicit parallelism of a population in evolutionary algorithms (EAs) provides an ideal platform for dealing with multiple tasks simultaneously. However, little effort has been made to explore …
This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solut…
This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance and demonstrates that a ratio-based indicator with infinite norm is the best for solut…
This article introduces a special multitasking optimization problem (MTOP) called the competitive MTOP (CMTOP). Its distinctive characteristics are that all tasks’ objectives are comparable, and …
Feature extraction is a critical issue in many machine learning systems. A number of basic fusion operators have been proposed and studied. This article proposes an evolutionary algorithm, called e…
This article proposes an evolutionary algorithm using multiple penalties and multiple local surrogates (MPMLS) for expensive constrained optimization. In each generation, MPMLS defines and optimize…