期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (2)
Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
Operational researchers and decision modelers have aspired to optimization technologies with a self-adaptive mechanism to cope with new problem formul......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (4)
Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization problem with complex routing and sequencing decisions under dyna......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (2)
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract ......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutiona......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent year......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
The multiobjective testing resource allocation problem (MOTRAP) is how to efficiently allocate the finite testing time to various modules, with the ai......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical go......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (4)
In recent years, numerous efficient and effective multimodal multiobjective evolutionary algorithms (MMOEAs) have been developed to search for multipl......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (4)
Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (2)
The problem of inferring nonlinear and complex dynamical systems from available data is prominent in many fields, including engineering, biological, s......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (2)
Under frequency fitness assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and i......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (4)
This article presents the second Part of a two-Part survey that reviews evolutionary dynamic optimization (EDO) for single-objective unconstrained con......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (3)
The key challenge of expensive optimization problems (EOP) is that evaluating the true fitness value of the solution is computationally expensive. A c......
期刊： IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2021; 25 (4)
In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current sol......
