Leiden, The Netherlands – The Natural Computing research cluster of LIACS, Leiden University is proud to announce that 9 of our papers and one hot-of-the-track paper have been accepted for presentation at the Genetic and Evolutionary Computation Conference (GECCO) 2024.
GECCO, renowned for its rigorous selection process and esteemed reputation, is the leading conference in the field of genetic and evolutionary computation.
Our Accepted Contributions
Our success at GECCO 2024 represents the diverse and in-depth research conducted by our team. Here’s an overview of our accepted papers:
1. Bridging Functions Across Domains
“Transfer Learning of Surrogate Models via Domain Affine Transformation”
Authors: Shuaiqun Pan, Diederick Vermetten, Manuel López-Ibáñez, Thomas Bäck, and Hao Wang
This paper introduces a novel method for transferring surrogate models between different tasks, potentially revolutionizing how we approach optimization problems. It shows that with a small dataset from the target domain, the adapted model outperforms both the original surrogate and a new one built from scratch.
2. Benchmarking
“Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics”
Authors: Diederick Vermetten, Carola Doerr, Hao Wang, Anna V. Kononova, and Thomas Bäck
This research provides a comprehensive benchmarking of 294 algorithm implementations, spotlighting the importance of rigorous benchmarking and the hidden potential within metaphor-based algorithms.
3. Symbolic Regression
“A Functional Analysis Approach to Symbolic Regression”
Authors: Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, and Anna V Kononova
Our team offers a novel algorithm—Fourier Tree Growing (FTG)—that advances traditional genetic programming methods for symbolic regression, demonstrating significant performance improvements.
4. Innovating Green Logistics
“A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem”
Authors: Ananta Anil Shahane, Niki van Stein, and Yingjie Fan
This paper proposes a fresh approach to the vehicle routing problem, emphasizing speed and environmental friendliness, marking a substantial stride in the quest for efficient and sustainable logistics solutions.
5. Automated Algorithm Selection
“Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization”
Authors: Konstantin Dietrich, Diederick Vermetten, Carola Doerr and Pascal Kerschke
This work proposes a novel Automated Algorithm Selection pipeline and analysis based on a large number of 2 and 5 dimensional functions of the Many-Affine BBOB suite combined with exploratory landscape analysis features.
6. Genetic Programming Software
“CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming”
Authors: Roman Kalkreuth, Thomas Bäck
This work proposes a modern implementation of Cartesian Genetic Programming.
7. Multi-objective Evolutionary Algorithms
“What performance indicator to use for self-adaptation in Multi-objective evolutionary algorithms”
Authors: Furong Ye, Frank Neumann, Jacob de Nobel, Aneta Neumann, Thomas Bäck
A comprehensive benchmarking study on self-adaptive mutation rates for multi-objective evolutionary algorithms.
8. Demistifying Nature-inspired Metaphors
“A Critical Analysis of Raven Roost Optimization”
Authors: Martijn Halsema, Diederick Vermetten, Thomas Bäck and Niki van Stein
This study critically examines the Raven Roost Optimization (RRO) algorithm within the broader context of nature-inspired metaheuristics, challenging its novelty and efficacy in the field of black-box optimization.
9. Modular Framework for Multi-objective Optimization
“Modular Optimization Framework for Mixed Expensive and Inexpensive Real-World Problems”
Authors: Roy de Winter, Thomas Bäck and Niki van Stein
A modular optimization framework is proposed that can deal with different problem characteristics encountered in real-world design problems. The common denominator of problems considered in this work is the computationally demanding objective function used to evaluate the designs, while some of the constraint functions can be inexpensive.
10. Hot of the Press
Hot of the press paper presentation for “Parallel Multi-Objective Optimization for Expensive and Inexpensive Objectives and Constraints”.
Authors: Roy de Winter, Bas Milatz, Julian Blank, Niki van Stein, Thomas Bäck, Kalyanmoy Deb
Expensive objectives and constraints are key characteristics of real-world multi-objective optimization problems. In practice, they often occur jointly with inexpensive objectives and constraints. This paper presents the Inexpensive Objectives and Constraints Self-Adapting Multi-Objective Constraint Optimization algorithm that uses Radial Basis function Approximations (IOC-SAMO-COBRA) for such problems.
See the original paper
Looking forward
The selection of these papers is a testament to our ongoing research efforts and the collaborative nature of our work at LIACS. It represents a step forward in our commitment to contribute to the field of natural computing.
We would like to thank all our researchers and collaborators for their contributions. We look forward to engaging with other professionals in the field and sharing our findings at GECCO 2024.
More details on each paper will be made available as we approach the conference date.
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