Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

In this work, we consider multitasking in the context of solving multiple optimization problems
simultaneously by conducting a single search process. The principal goal when dealing with …

Evolutionary multitask optimization (EMTO) has recently attracted widespread attention in
the evolutionary computation community, which solves two or more tasks simultaneously to …

Fuzzy Cognitive Maps (FCMs) are widely used to simulate complex systems. However, they
cannot handle nonlinear relationships or time delays/lags, nor can they fully represent …

Traditional evolution algorithms tend to start the search from scratch. However, real-world
problems seldom exist in isolation and humans effectively manage and execute multiple …

Among various soft computing approaches for time series forecasting, fuzzy cognitive maps
(FCMs) have shown remarkable results as a tool to model and analyze the dynamics of …

Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA)
that aims to optimize multiple tasks simultaneously within a same problem and output the …

Fuzzy cognitive maps have achieved significant success in time series modeling and
forecasting. However, fuzzy cognitive maps still contain weakness to handle the …

Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the
excellent performance in dynamic system modeling. However, existing time series prediction …

Due to the multilayer nature of real-world systems, the problem of inferring multilayer
network structures from nonlinear and complex dynamical systems is prominent in many …