IEEE Transactions on Evolutionary Computation

Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of ...

This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, decomposition methods and hybridization methods, such as memetic ...

Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized ...

Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been ...

Expensive constrained optimization refers to problems where the calculation of the objective and/or constraint functions are computationally intensive due to the involvement of complex physical experiments or numerical simulations. Such expensive problems ...

Multimodality is commonly seen in real-world multiobjective optimization problems (MOPs). In such optimization problems, namely, multimodal MOPs (MMOPs), multiple decision vectors can be projected to the same solution in the objective space (i.e., there ...

Matrix operation is easy to be paralleled by hardware, and the memristor network can realize a parallel matrix computing model with in-memory computing. This article proposes a matrix-friendly genetic algorithm (MGA), in which the population is ...

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to ...

Point-set registration is widely used in computer vision and pattern recognition. However, it has become a challenging problem since the current registration algorithms suffer from the complexities of the point-set distributions. To solve this problem, we ...

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 burden and poor generalization ability. To overcome the limitations, ...

Innovization is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems. A recent study has shown that a chronological sequence of nondominated solutions obtained ...

Recent studies show that deep neural networks are vulnerable to adversarial attacks in the form of subtle perturbations to the input image, which leads the model to output wrong prediction. Such an attack can easily succeed by the existing white-box ...

We introduce an evolutionary algorithm called recombinator-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all ...

We present techniques for improving the efficiency of ND-trees in the solution of the dynamic nondominance problem, i.e., for building and maintaining a Pareto archive. We propose algorithms for (re)building a tree from a set of nondominated points, ...

Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection is an effective preprocessing technique for enhancing the ...

Due to the high variability and uncertainty of the wind speed, an interval forecast can provide more information for decision makers to achieve a better energy management compared to the traditional point forecast. In this article, a knee-based lower ...

This article deals with a practical distributed hybrid differentiation flowshop scheduling problem (DHDFSP) for the first time, where manufacturing products to minimize makespan criterion goes through three consecutive stages: 1) job fabrication in first-...

Multiobjective evolutionary algorithms (EAs) based on decomposition are becoming successful and popular. Particularly, a conical area EA (CAEA) was developed to heighten the convergence and population diversity of decomposition-based algorithms for bi-...

Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious since training and evaluating complete neural architectures ...

Various evolutionary algorithms (EAs) have been proposed to address feature selection (FS) problems, in which a large number of fitness evaluations are needed. With the rapid growth of data scales, the fitness evaluation becomes time consuming, which ...

Dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often conflicting) objectives that are changing over time. Recently, there are a number of promising algorithms proposed based on transfer learning methods to solve DMOPs. ...

All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the protein&#x2013;protein ...

In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary ...

Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, ...

Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due to their costly evaluation. Building cheap surrogate models to replace the expensive real models has been proved to be a practical way to reduce the number ...

Quality-diversity (QD) algorithms evolve behaviorally diverse and high-performing solutions. To illuminate the elite solutions for a space of behaviors, QD algorithms require the definition of a suitable behavior space. If the behavior space is high-...

In offline data-driven multiobjective optimization, no new data are available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be ...

Seismic data acquired in a desert environment often have a low signal-to-noise ratio, posing a significant challenge to seismic processing, imaging, and inversion. Nonlinear beamforming is one effective method that uses local second-order mathematical ...