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Quantum computing, albeit readily available as hardware or emulated on the cloud, is still far from being available in general regarding complex programming paradigms and learning curves. This vision paper introduces Classi|Q⟩, a translation framework ...

Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages, including ...

The benchmark problem of lunar lander landing site selection is a nonlinear constrained optimization problem introduced as a challenge for the 2018 Evolutionary Computation Symposium competition. In the single-objective variant, the task is to find the ...

Neural Architecture Search (NAS) is a promising approach to automate the design of neural network architectures, which can find architectures that perform better than manually designed ones. Hardware-aware NAS is a real-world application of NAS where the ...

Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades. However, existing algorithms often encounter computational challenges in large-scale scenarios, primarily attributed to the absence of hardware ...

The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with simple networks and incrementally evolving both their topologies and ...

Hyperspectral imaging brings important opportunities in a variety of fields due to the unprecedented amount of information it captures in numerous narrow and contiguous spectral bands. However, the high spectral and spatial dimensionality of ...

Nowadays, trajectory data is widely available and used in various real-world applications such as urban planning, navigation services, and location-based services. However, publishing trajectory data can potentially leak sensitive information about ...

Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessarily have a meaningful order, and ...

Experience shows that typical evolutionary algorithms can cope well with stochastic disturbances such as noisy function evaluations. In this first mathematical runtime analysis of the (1 + λ) and (1, λ) evolutionary algorithms in the presence of prior ...

In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as safe optimization and formulated as a specialized type of constrained ...

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness ...

This study proposes Evolutionary Diversity Optimisation (EDO) to Lower the Probability of Detection (LPD) in directed wireless networks. LPD communication aims to communicate between authorised parties, however minimises the probability that an intruder ...

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have been proposed to bring out the optimization performance of the CMA-ES on ...

Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterative search algorithms and study ...

Recently surrogate functions based on the tail inequalities were developed to evaluate the chance constraints in the context of evolutionary computation and several Pareto optimization algorithms using these surrogates were successfully applied in ...

We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem. Block-coordinate descent, where an optimization problem is decomposed into k ...

Quality Diversity (QD) aims to evolve a population of solutions that are both diverse and of high quality. The Map-Elites QD approach partitions the search space according to a feature space and stores the best solution for each feature. Bossek & Sudholt ...

Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the parents,...

Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, ...