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Differential evolution (DE) is a population-based stochastic parallel optimization algorithm that has been successfully applied to solve many optimization problems in Engineering. It is well known that DE performance depends strongly on its control parameters such as mutation factor, crossover probability, and population size. Although the former two control parameters have received a lot of attention, the later has not received the attention it deserves. Like many evolutionary algorithms (EAs), the population size in DE is important as it influences whether good solutions can be found and the time it takes to find them. Generally, the population size of DE algorithm is specified by the user and remains fixed during the run. Therefore, inappropriate choice of the population size may severely hinder the performance of DE algorithm. Previously, DE population size was treated as problem independent. However, recent research suggested that population size is problem dependent; in particular when dealing with high dimension problems. This paper investigates the impact of the population size on the performance of DE when applied to the optimal tuning of power system stabilizers (PSSs). Several population sizes have been investigated and their impact on the performance of the PSSs are presented. The effectiveness of these controllers is assessed using modal analysis and validated by time domain simulations.

ECAI 2020

Impact of Population Size on the Performance of DE Based Power System Stabilizers

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AMA Research Challenge 2025 

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technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Equal Pay for Equal Competences : A Statistical  Approach to Address Equal Pay Gap

On Minimizing Diagonal Block-wise Differences for  Neural Network Compression

Deep Density-aware Count Regressor

DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction

Robot self other distinction active inference meets neural networks learning in a mirror

Minimality of Combined Qualitative Constraint Networks

SI-AGAN: Spatial Interpolation with Attentional Generative Adversarial Networks for Environment Monitoring

The combination of logic and probability is very useful for modeling domains with complex and uncertain relationships among entities. Machine learning approaches based on such combinations have recently achieved important results, originating the fields of Statistical Relational Learning, Probabilistic Inductive Logic Programming and, more generally, Statistical Relational Artificial Intelligence.
The tutorial will concentrate on Probabilistic Logic Programming, a form of Probabilistic Programming that is receiving an increasing attention for its ability to combine powerful knowledge representation with Turing completeness.

This tutorial will introduce probabilistic logic programming and overview the main systems for learning models in these formalisms both in terms of parameters and of structure. The tutorial includes a significant hands-on experience with the systems ProbLog2 and cplint using the web applications https://dtai.cs.kuleuven.be/problog/ and http://cplint.eu that the attendants can access with their notebooks via wifi.


Probabilistic Inductive Logic Programming

Towards an Explainable Argumentation-based Agent

"We propose a Generative Adversarial Networks (GANs) based architecture for cloud removal from satellite imagery. Data used for training comprises of visible light RGB and near-infrared (NIR) band images. The novelty lies in the structure of discriminator in GANs architecture. We propose the discriminator to compare generated and target cloud-free RGB images concatenated with their edge-filtered versions. The motivation behind this approach is that such a discriminator drives the
learning of the generator towards better identification of objects
covered by clouds. In the experimental results, we compare our
method with the benchmark solution, and demonstrate that our approach is superior in performing cloud removal for various cloud intensity levels."

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