optimization

A Granular Ball–Ant Colony Optimization Framework Enhanced by Variable Neighborhood Search for the Multi-Depot Half-Open Time-Dependent EVRP (2026)

Annual Conference on Genetic and Evolutionary Computation Authors Yingkai Xu, Anikó Kopacz, Camelia Chira Abstract The widespread adoption of electric vehicles (EVs) in urban logistics has led to the Electric Vehicle Routing Problem (EVRP) emerging as a prominent topic in combinatorial optimization research. To improve the efficiency of urban logistics distribution and minimize operational…

Polarity Related Influence Maximization through Multi-Agent Reinforcement Learning (2026)

Proceedings of the 18th International Conference on Agents and Artificial Intelligence Authors Anikó Kopacz, Camelia Chira Abstract Influence maximization is a network optimization problem, which consists of selecting nodes as sources while maximizing the spread of information. The source nodes that are initially activated form the seed set. Polarity-related influence maximization accounts for having…

Network Optimization Problem Applicable for Breast Cancer Screening Cost Minimization (2025)

We investigate the problem of breast cancer screening optimization, using various techniques applicable in domains where the data format is not defined in advance. The aim is to minimize the cost related to the screening of patients while maximizing the beneficial effect of the process regarding some key breast cancer indices. Our model can…

Log-Loss Optimization for Boosting a Nash Equilibrium Decision Tree (2025)

Decision trees rank among the most popular classification tools, employed in practical applications due to their known efficiency. A Nash equilibrium-based decision tree splits node data using the Nash equilibrium concept. Boosting is a technique that is used to enhance the performance of a classifier by allowing an in-depth exploration of the data. This…

A Pseudo-Deterministic Noisy Extremal Optimization algorithm for the pairwise connectivity Critical Node Detection Problem (2024)

The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in…

Multi-objective Optimization for Multi-Robot Path Planning on Warehouse Environments (2023)

Today, robots can be found in almost any field. Examples include robots for transporting materials in hospitals and warehouses, surveillance, intelligent laboratories and space exploration. Whatever the reason for moving the robot and whatever its location, all robot applications anywhere require path calculation. In this paper, we address the problem of collision-free path planning…

Identification of influential nodes with Shapley Influence Maximization Extremal Optimization algorithm (2023)

The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent cascade model is considered as a cooperative game, where players seek…

A Game Theoretic Decision Tree for Binary Classification (2022)

Decision trees are some of the most popular and intuitive classification techniques. Based on the recursive division of the data, the goal is to ultimately identify regions in the space in which most instances belong to the same class. This paper proposes a game-theoretic decision tree using a two-player game to determine the splitting…

A New Nash-Probit Model for Binary Classification (2021)

The Nash equilibrium is used to estimate the parameters of a Probit binary classification model transformed into a multiplayer game. Each training data instance is a player of the game aiming to maximize its own log likelihood function. The Nash equilibrium of this game is approximated by modifying the Covariance Matrix Adaptation Evolution Strategy…

On the use of evolutionary algorithms for test case prioritization in regression testing considering requirements dependencies (2021)

Nowadays, software systems encounter repeated modifications in order to satisfy any requirement regarding a business change. To assure that these changes do not affect systems' proper functioning, those parts affected by the changes need to be retested, minimizing the negative impact of performed modifications on another part of the software. In this research, we…