IEEE Transactions on Evolutionary Computation

Multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar in the objective space but totally different in the decision space. While some ...

The robustness of complex networks is of great significance. Great achievements have been made in robustness optimization based on single measures, however, such networks may still be vulnerable to multiple attack scenarios. Therefore, recently, ...

Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming (GP). Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous ...

Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutionary algorithms (EAs) due to the existence of multiple constraints and objectives. When the number of ...

This study develops an adaptive multilevel prediction (AMLP) method to detect and track multiple global optima over time. First, it formulates a multilevel prediction approach in which a higher level prediction improves the accuracy of the lower level ...

The key challenge of expensive optimization problems (EOP) is that evaluating the true fitness value of the solution is computationally expensive. A common method to deal with this issue is to seek for a less expensive surrogate model to replace the ...

Operational researchers and decision modelers have aspired to optimization technologies with a self-adaptive mechanism to cope with new problem formulations. Self-adaptive mechanisms not only free users from low-level and complex development tasks to ...

A green supply chain, as one of the most critical low-carbon strategies, has been widely studied along with evolutionary game theory, for which governmental policies, such as a carbon tax, subsidies, or penalties, have been frequently applied; however, ...

Evolutionary neural architecture search (ENAS) can automatically design the architectures of deep neural networks (DNNs) using evolutionary computation algorithms. However, most ENAS algorithms require an intensive computational resource, which is not ...

The multiobjective testing resource allocation problem (MOTRAP) is how to efficiently allocate the finite testing time to various modules, with the aim of optimizing system reliability, testing cost, and testing time simultaneously. To deal with this ...

Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic ...

The design of the evolutionary algorithm with learning capability from past search experiences has attracted growing research interests in recent years. It has been demonstrated that the knowledge embedded in the past search experience can greatly speed ...

The artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields. The detector set is the core knowledge set, and the AIS application effects are mainly determined by the ...

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast ...