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ACM 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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