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ORCIDACM Transactions on Evolutionary Learning and Optimization, Volume 2
Volume 2, Number 1, March 2022
- Sukrit Mittal
, Dhish Kumar Saxena, Kalyanmoy Deb, Erik D. Goodman:
A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization. 1:1-1:29 - Youhei Akimoto, Yoshiki Miyauchi, Atsuo Maki:
Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing Control. 2:1-2:32 - Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck:
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics. 3:1-3:29 - Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo, Chun-Yi Lee:
Reusability and Transferability of Macro Actions for Reinforcement Learning. 4:1-4:16
Volume 2, Number 2, June 2022
- H. David Mathias, Annie S. Wu, Daniel Dang:
Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation. 5:1-5:30 - William B. Langdon:
Deep Genetic Programming Trees Are Robust. 6:1-6:34 - Penny Faulkner Rainford, Barry Porter:
Code and Data Synthesis for Genetic Improvement in Emergent Software Systems. 7:1-7:35 - Mickaël Binois, Nathan Wycoff:
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization. 8:1-8:26
Volume 2, Number 3, September 2022
- Patrick Spettel, Hans-Georg Beyer:
On the Design of a Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds. 9:1-9:32 - Yasha Pushak, Holger H. Hoos:
AutoML Loss Landscapes. 10:1-10:30 - Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Analysis of Evolutionary Diversity Optimization for Permutation Problems. 11:1-11:27
Volume 2, Number 4, December 2022
- Alexander Wild, Barry Porter:
Multi-donor Neural Transfer Learning for Genetic Programming. 12:1-12:40 - Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm. 13:1-13:39 - Renato Miranda Filho, Anísio M. Lacerda, Gisele L. Pappa:
Explainable Regression Via Prototypes. 14:1-14:26
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