{"id":672,"date":"2025-03-08T12:45:49","date_gmt":"2025-03-08T11:45:49","guid":{"rendered":"https:\/\/www.evostar.org\/2025\/?page_id=672"},"modified":"2025-03-10T21:59:55","modified_gmt":"2025-03-10T20:59:55","slug":"evoapplications-accepted-papers","status":"publish","type":"page","link":"https:\/\/www.evostar.org\/2025\/evoapplications-accepted-papers\/","title":{"rendered":"EvoApplications Accepted Papers"},"content":{"rendered":"\n

Long talks<\/h2>\n\n\n\n
  • Social Interpretable Reinforcement Learning (EML joint track)<\/strong>
    Leonardo Lucio Custode and Giovanni Iacca<\/em><\/li>\n
  • Adjacent Distance Matrix-based Competitive Swarm Optimizer
    Yang Cao, Rui Zhong, Jun Yu and Masaharu Munetomo<\/em><\/li>\n
  • Methodology for Designing Injection Molds: Data Mining and Multi-Objective Optimization
    Ant\u00f3nio Gaspar-Cunha, Jo\u00e3o Melo, Tom\u00e1s Marques and Ant\u00f3nio Pontes<\/em><\/li>\n
  • Fair Ambulance Allocation via Multi-Objective Evolutionary Optimization
    Torjus Kallekleiv and Ole Jakob Mengshoel<\/em><\/li>\n
  • Climbing the tower of meta-mutations – the role of higher-order mutations
    Bruno Ga\u0161perov and Branko \u0160ter<\/em><\/li>\n
  • Real Application Challenges in Evolutionary Optimization? People!
    Tobias Rodemann and Christiane Attig<\/em><\/li>\n
  • EDCA – An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines (EML joint track)<\/strong>
    Joana Sim\u00f5es and Jo\u00e3o Correia<\/em><\/li>\n
  • Multi-Objective Evolutionary Optimization of Virtualized Fast Feedforward Networks
    Renan Beran Kilic, Kasim Sinan Yildirim and Giovanni Iacca<\/em><\/li>\n
  • We are Sending you Back… to the Optimum! Fuzzy Time Travel Particle Swarm Optimization
    Daniele Papetti, Andrea Tangherloni, Vasco Coelho, Daniela Besozzi, Paolo Cazzaniga and Marco S. Nobile<\/em><\/li>\n
  • Multi-Purpose Image Filter Evolution Using Cellular Automata and Function-Based Conditional Rules
    Michal Bidlo and Ivana Saranova<\/em><\/li>\n
  • Injecting Combinatorial Optimization into MCTS: Application to the Board Game boop.
    Florian Richoux<\/em><\/li>\n
  • A PSO-based MPPT with Dynamic Monitoring Reset for PV Systems
    Igor de Matos da Rosa, Alison R Panisson and Lenon Schmitz<\/em><\/li>\n
  • Search Trajectory Networks Applied to a Real-world Parallel Batch Scheduling Problem
    Francesca Da Ros, Luca Di Gaspero, Marie-Louise Lackner, Nysret Musliu and Michael Soprano<\/em><\/li>\n
  • Addressing Radiotherapy Scheduling with a Bin Packing Problem Formulation: A Comparative Study of Exact Solvers and Genetic Algorithms
    Chiara Camilla Migliore Rambaldi, David Stanicel, Marco Roveri and Giovanni Iacca<\/em><\/li>\n
  • Optimizing Camera Placement for Chicken Farm Monitoring
    Kyriacos Mosphilis and Vassilis Vassiliades<\/em><\/li>\n
  • GPBus: Genetic Programming based Automated Machine Learning for Bus Delay Prediction
    \u00c1ngel Fuentes-Almoguera, Carlos Garc\u00eda-Mart\u00ednez and Gabriel Luque<\/em><\/li>\n
  • Adaptive Local Search for Real-World Multi-Echelon Inventory Control
    Agathe M\u00e9taireau Manche, Clarisse Dhaenens, Nadarajen Veerapen and Manuel Davy<\/em><\/li>\n
  • Trace-Elites: better Quality-Diversity with Multi-Point Descriptors
    Harald Michael Ludwig, Ane Espeseth and Eric Medvet<\/em><\/li>\n
  • Grammatical Feature Construction for Enhanced Interpretability in Breast Cancer Classification
    Yumnah Hasan, Allan de Lima, Darian Fern\u00e1ndez de Bulnes, Douglas Motadias and Conor Ryan<\/em><\/li>\n
  • Emergent kin selection of altruistic feeding behaviour via non-episodic neuroevolution
    Max Taylor-Davies, Gautier Hamon, Timoth\u00e9 Boulet and Clement Moulin-Frier<\/em><\/li>\n
  • Multi-Tree Genetic Programming for Dynamic Tugboat Scheduling
    Xinxin Xu, Fangfang Zhang, Yi Mei, Mengjie Zhang, Huili Gong and Xiangqian Ding<\/em><\/li>\n
  • Analysis of Illicit Drug Mixtures at Festivals Using Portable Near-Infrared Spectroscopy with Genetic Programming
    Steven Dockter, Qi Chen, Deepak Karunakaran and Yongshi Deng<\/em><\/li>\n
  • Evolving Dynamic Fault Mitigation Strategies in a Robot Swarm for Collective Transport
    Suet Lee and Sabine Hauert<\/em><\/li>\n
  • A Survey of Modern Hybrid Particle Swarm Optimization Algorithms
    Matteo Grazioso, Chiara Gallese, Leonardo Vanneschi and Marco S. Nobile<\/em><\/li>\n
  • A Communication-aware and Energy-efficient Genetic Programming based Method for Dynamic Resource Allocation in Clouds
    Zhengxin Fang, Hui Ma, Gang Chen, Sven Hartmann and Shiping Chen<\/em><\/li>\n
  • An Investigation of Structural Bias in Particle Swarm Optimization
    David Ibehej and Jakub Kudela<\/em><\/li>\n
  • Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
    Kevin Sim, Quentin Renau and Emma Hart<\/em><\/li>\n
  • A Genetic Algorithm Approach for Aggregation of Residential Electricity Prosumers\u2019 Flexibility
    Vahid Rasouli, \u00c1lvaro Gomes and Carlos Henggeler Antunes<\/em><\/li>\n
  • Optimizing Dietary Plans Using Evolutionary Algorithms
    Iqra Azfar, Rabia Shahab, Javeria Azfar and Syeda Saleha Raza<\/em><\/li>\n
  • Evaluating the Impact of Hysteretic Phenomena and Implementation Choices on Energy Consumption in Evolutionary Algorithms
    Carlos Cotta and Jes\u00fas Mart\u00ednez Cruz<\/em><\/li>\n
  • Proposal of Efficient Particle Swarm Optimization for Constrained Optimization Problems
    So Fukuhara and Masao Arakawa<\/em><\/li>\n
  • Measuring energy consumption of BBOB fitness functions
    Jj Merelo, Gustavo Romero L\u00f3pez and Mario Garcia Valdez<\/em><\/li>\n
  • A Multi-Agent System for Optimal Train Scheduling in Single-Track Railways
    Raziyeh Moghaddas, Fabio Caraffini and Monika Seisenberger<\/em><\/li>\n
  • Geometric Particle Swarm Optimization in Program Trace Optimization
    Alberto Moraglio and James McDermott<\/em><\/li>\n
  • Generate more than one child in your co-evolutionary semi-supervised learning GAN (EML joint track)<\/strong>
    Francisco Jos\u00e9 Sede\u00f1o, Jamal Toutouh and Francisco Chicano<\/em><\/li>\n
  • Evolving RNNs for Stock Forecasting: A Low Parameter Efficient Alternative to Transformers (EML joint track)<\/strong>
    Zimeng Lyu, Devroop Kar, Matthew Simoni, Rohaan Nadeem, Avinash Bhojanapalli, Hao Zhang and Travis Desell<\/em><\/li>\n
  • Robust search for the underlying objectives in black-box games with binary outcomes
    Dmytro Vitel, Alessio Gaspar and Paul Wiegand<\/em><\/li>\n
  • Analyzing the Effects of Memetic Variations on Convergence in Overlapping Swarm Intelligence
    Nathan Patera and John Sheppard<\/em><\/li>\n
  • Designing Hardware-Friendly Hash Functions for Network Security Using Cartesian Genetic Programming
    Mujtaba Hassan, Jo Vliegen, Stjepan Picek and Nele Mentens<\/em><\/li>\n
  • Genetic Programming with Co-operative Co-evolution for Feature Manipulation in Basal Cell Carcinoma Identification
    Taran Cyriac John, Qurrat Ul Ain, Harith Al-Sahaf and Mengjie Zhang<\/em><\/li>\n
  • Greater AI Design Control Aids Evolution of Computational Materials
    Piper Welch, Monica Li, Shawn Beaulieu, Annie Xia, Dong Wang, Medha Goyal, Atoosa Parsa, Corey O’Hern, Rebecca Kramer and Josh Bongard<\/em><\/li>\n
  • Scalable Evolution of Logically Independent Polycomputational Materials
    Piper Welch, Atoosa Parsa, Shawn Beaulieu, Corey O’Hern, Rebecca Kramer and Josh Bongard<\/em><\/li>\n
  • A Symbolic Regression Screening Approach within Peptide Optimisation
    Aidan Murphy, Mark Kocherovsky, Nir Dayan, Illya Miralavy, Assaf Gilad and Wolfgang Banzhaf<\/em><\/li>\n
  • Evolutionary Bias Identification with Embeddings
    Arthur Buzelin, Yan Aquino, Victoria Estanislau, Pedro Bento, Lucas Dayrell, Samira Malaquias, Caio Santana Trigueiro, Guilherme Evangelista, Caio Grossi, Pedro Rigueira, Luisa Gontijo Porfirio, Marcelo Sartori Locatelli, Wagner Meira Jr and Gisele Pappa<\/em><\/li>\n
  • The More the Merrier: On Evolving Five-valued Spectra Boolean Functions
    Claude Carlet, Marko \u0110urasevi\u0107, Domagoj Jakobovic, Luca Mariot and Stjepan Picek<\/em><\/li>\n
  • Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
    Haoran Yin, Anna Kononova, Thomas B\u00e4ck and Niki Van Stein<\/em><\/li>\n
  • Stalling in Space: Attractor Analysis for any Algorithm
    Sarah L. Thomson, Quentin Renau, Diederick Vermetten, Emma Hart, Niki van Stein and Anna V. Kononova<\/em><\/li>\n
  • Into the Black Box: Mining Variable Importance with XAI (EML joint track)<\/strong>
    Kelly Hunter, Sarah L. Thomson and Emma Hart<\/em><\/li>\n
  • The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm
    Francesco Rusin, Jan Fiala, Julian Sanker and Aniko Ekart<\/em><\/li>\n
  • FedGP: Genetic Programming for Evolutionary Aggregation in Federated Learning with Non-IID data
    Elia Pacioni, Francisco Fernandez De Vega and Davide Calvaresi<\/em><\/li>\n
  • Estimation of total body fat using symbolic regression and evolutionary algorithms.
    Jose Manuel Mu\u00f1oz, Odin Moron-Garcia, Omar Costilla-Reyes and J. Ignacio Hidalgo<\/em><\/li>\n\n\n\n

    Short talks<\/h2>\n\n\n\n
  • A Coach-Based Quality-Diversity Approach for Multi-Agent Interpretable Reinforcement Learning
    Erik Nielsen, Andrea Ferigo and Giovanni Iacca<\/em><\/li>\n
  • Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
    Stefano Genetti, Alberto Longobardi and Giovanni Iacca<\/em><\/li>\n
  • An innovative approach for managing the water requirements of fig trees using artificial intelligence
    Josefa D\u00edaz \u00c1lvarez, Francisco Chavez and Maria Jos\u00e9 Mo\u00f1ino Espino<\/em><\/li>\n
  • Variable-Size Genetic Network Programming for Portfolio Optimization with Trading Rules
    Fabian K\u00f6hnke and Christian Borgelt<\/em><\/li>\n
  • Building Cross-Sectional Trading Strategies via Geometric Semantic Genetic Programming
    Kritpol Bunjerdtaweeporn and Alberto Moraglio<\/em><\/li>\n
  • Probing LLMs on Optimization Problems: Can They Recall and Interpret Problem Features?
    Francesca Da Ros, Luca Di Gaspero and Kevin Roitero<\/em><\/li>\n
  • A Genetic Algorithm-Based Parameter Selection for Communication-Efficient Federated Learning
    Mir Hassan, Kasim Sinan Yildirim and Giovanni Iacca<\/em><\/li>\n
  • Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming
    Wenlong Fu, Qi Chen, Bing Xue and Mengjie Zhang<\/em><\/li>\n
  • Evolutionary Computation for Causality-Driven Feature Selection: A Preliminary Study
    Emanuele Nardone, Tiziana D’Alessandro, Claudio De Stefano and Francesco Fontanella<\/em><\/li>\n
  • Inferring Reaction Elasticities from Metabolic Correlations in Cells through Multi-objective Evolutionary Optimization
    Arthur Lequertier, Alberto Tonda and Wolfram Liebermeister<\/em><\/li>\n
  • Hybridization of techniques based on Genetic Algorithms and Neural Networks to determine the water requirements of fig trees.
    Francisco Ch\u00e1vez de la O, Josefa D\u00edaz \u00c1lvarez and Mar\u00eda Jos\u00e9 Mo\u00f1ino Espino<\/em><\/li>\n
  • Optimizing the logistics operations of distribution network operators from a multinational electric utility company
    Diego Dantas Almeida, Mariana Azevedo, Victor Vieira, Nelson Ion de Oliveira, Anna Giselle C\u00e2mara Dantas Ribeiro Rodrigues, Leonardo Bezerra, Lucas Nunes, Tha\u00eds Alves de Mendon\u00e7a and Rodrigo Manfredini<\/em><\/li>\n
  • Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
    Quentin Renau and Emma Hart<\/em><\/li>\n
  • Hybrid Optimization of Horizontal Alignments in European Terrains: A Comparative Study
    Ane Espeseth, Martin Ju\u0159\u00ed\u010dek, Harald Michael Ludwig and Tea Tu\u0161ar<\/em><\/li>\n
  • Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management
    Catalina Jaramillo Gonzalez, Julian Togelius and Paul Squires<\/em><\/li>\n
  • Using Local Correlation Between Objectives to Detect Problem Modality
    Tea Tu\u0161ar and Jordan N. Cork<\/em><\/li>\n
  • Open and Closed-source Models for LLM-generated Metaheuristics Solving Engineering Optimization Problem
    Roman Senkerik, Adam Viktorin, Tomas Kadavy, Jozef Kovac, Peter Janku, Libor Pekar, Hubert Guzowski, Maciej Smolka, Aleksander Byrski and Michal Pluhacek<\/em><\/li>\n","protected":false},"excerpt":{"rendered":"

    Long talks Social Interpretable Reinforcement Learning (EML joint track)Leonardo Lucio Custode and Giovanni Iacca Adjacent Distance Matrix-based Competitive Swarm OptimizerYang Cao, Rui Zhong, Jun Yu and Masaharu Munetomo Methodology for Designing Injection Molds: Data Mining and Multi-Objective OptimizationAnt\u00f3nio Gaspar-Cunha, Jo\u00e3o Melo, Tom\u00e1s Marques and Ant\u00f3nio Pontes Fair Ambulance Allocation via Multi-Objective Evolutionary OptimizationTorjus Kallekleiv and […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-672","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/pages\/672","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/comments?post=672"}],"version-history":[{"count":15,"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/pages\/672\/revisions"}],"predecessor-version":[{"id":727,"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/pages\/672\/revisions\/727"}],"wp:attachment":[{"href":"https:\/\/www.evostar.org\/2025\/wp-json\/wp\/v2\/media?parent=672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}