This work presents interesting multi-point search algorithms exploiting several surrogate models, implemented in MI-NAMO, the multi-disciplinary optimization platform of Cenaero. Many types of surrogate models are used in the literature with their own ...

Dynamic Compartmental Models (DCM) can be used to study the population dynamics of Multi- and Many-objective Optimization Evolutionary Algorithms (MOEAs). These models track the composition of the instantaneous population by grouping them in ...

Multi-objective optimization problems in the real world often involve a decision maker who has certain preferences for the objective functions. When such preferences can be expressed as a reference point, the goal of optimization changes from generating ...

Many real-world optimization problems involve both multiple objectives and constraints. Although constraint handling in multiobjective optimization has been considered in the literature, there is still a high demand for more advanced and versatile ...

The performance comparison of multi-objective evolutionary algorithms (MOEAs) has been a broadly studied research area. For almost two decades, quality indicators (QIs) have been employed to quantitatively compare the Pareto front approximations ...

This paper proposes a new discrete optimization benchmark 100b-Digit, a binary discretized version for the 100-Digit Challenge. The continuous version 100-Digit Challenge utilizing continuous input parameters for a fitness function was suggested for the ...

Per-Instance Algorithm Selection and Automatic Algorithm Configuration have recently gained important interests. However, these approaches face many limitations. For instance, the performance of these methods is deeply influenced by factors like the ...

Some of the modern evolutionary multiobjective algorithms have a high computational complexity of the internal data processing. To further complicate this problem, researchers often wish to alter some of these procedures, and to do it with little ...

This paper describes the approach for calibration of environmental models with the presence of time and quality restrictions. Advantages of the suggested strategy are based on two main concepts. The first advantage was provided by reducing the overall ...

Aiming at the problem of oversubscription of data relay access request of user stars in future Space-Based Information System, the problem of resource scheduling optimization for data relay satellite system with microwave and laser hybrid links is ...

Border detection of melanoma and other skin lesions from images is an important step in the medical image processing pipeline. Although this task is typically carried out manually by the dermatologists, some recent papers have applied evolutionary ...

Detecting the environmental changes in dynamic optimization problems is an essential phase for a dynamic evolutionary algorithm. By determining the time points of change in the problem, the evolutionary algorithm is capable of adapting and responding to ...

Multi-modal multi-objective problems (MMMOPs) have two or more distinct Pareto-optimal sets (PSs) mapping to the same Pareto-front (PF). Identifying all such PSs assists in informed decision-making. However, existing multi-objective evolutionary ...

The set of primitive operations available to a generative hyper-heuristic can have a dramatic impact on the overall performance of the heuristic search in terms of efficiency and final solution quality. When constructing a primitive set, users are faced ...

Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. Examples include modeling the evolution of relationships and communities in a social network or tracking the activity of users within an enterprise ...

There are situations where the need for optimisation with a global precision tolerance arises - for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ϵ > 0 can be predefined ...

Learning Classifier Systems (LCSs) are a unique machine learning paradigm. The probably most well-known and investigated instance of these is XCS. LCSs, and with them, XCS, have developed in parallel to mathematically more rigorously founded paradigms ...

Connected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind is the so-called Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various ...

Hyperparameter tuning is an important mixed-integer optimisation problem, especially in the context of real-world applications such as games. In this paper, we propose a function suite around hyperparameter optimisation of game AI based on the card game ...

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In the ...