pwc-archive/papers-with-abstracts huggingface dataset:
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
Authors: Xiaoming Xue, Liang Feng, Yinglan Feng, Rui Liu, Kai Zhang, Kay Chen Tan
Date: 2025-03-27 00:00:00
Tasks: Transfer Learning

Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization. However, a pressing issue in this field is that the invention of new ETO algorithms has far outpaced the development of fundamental theories needed to clearly understand the key factors contributing to the success of these algorithms for effective generalization. In response to this challenge, this study aims to establish theoretical foundations for analogy-based ETO, specifically to support various algorithms that frequently reference a key concept known as similarity. First, we introduce analogical reasoning and link its subprocesses to three key issues in ETO. Then, we develop theories for analogy-based knowledge transfer, rooted in the principles that underlie the subprocesses. Afterwards, we present two theorems related to the performance gain of analogy-based knowledge transfer, namely unconditionally nonnegative performance gain and conditionally positive performance gain, to theoretically demonstrate the effectiveness of various analogy-based ETO methods. Last but not least, we offer a novel insight into analogy-based ETO that interprets its conditional superiority over traditional evolutionary optimization through the lens of the no free lunch theorem for optimization.
Multi-objective Pseudo Boolean Functions in Runtime Analysis: A Review
Authors: Zimin Liang, Miqing Li
Date: 2025-03-24 00:00:00
Tasks: Evolutionary Algorithms

Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the test problems commonly used in the theoretical analysis are predominantly limited to problems with heavy ``artificial'' characteristics (e.g., symmetric objectives and linear Pareto fronts), which may not be able to well represent realistic scenarios. In this paper, we survey commonly used multi-objective functions in the theory domain and systematically review their features, limitations and implications to practical use. Moreover, we present several new functions with more realistic features, such as local optimality and nonlinearity of the Pareto front, through simply mixing and matching classical single-objective functions in the area (e.g., LeadingOnes, Jump and RoyalRoad). We hope these functions can enrich the existing test problem suites, and strengthen the connection between theoretic and practical research.
An Approach to Analyze Niche Evolution in XCS Models
Authors: Pier Luca Lanzi
Date: 2025-03-19 00:00:00

We present an approach to identify and track the evolution of niches in XCS that can be applied to any XCS model and any problem. It exploits the underlying principles of the evolutionary component of XCS, and therefore, it is independent of the representation used. It also employs information already available in XCS and thus requires minimal modifications to an existing XCS implementation. We present experiments on binary single-step and multi-step problems involving non-overlapping and highly overlapping solutions. We show that our approach can identify and evaluate the number of niches in the population; it also show that it can be used to identify the composition of active niches to as to track their evolution over time, allowing for a more in-depth analysis of XCS behavior.
Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization
Authors: Xian-Rong Zhang, Yue-Jiao Gong, Zhiguang Cao, Jun Zhang
Date: 2025-03-17 00:00:00
Tasks: Evolutionary Algorithms, global-optimization, Transfer Learning

In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantly when applied to large-scale optimization problems (LSOPs). To address the challenge, this paper proposes an offline DDEA named DSKT-DDEA. DSKT-DDEA leverages multiple islands that utilize different data to establish diverse surrogate models, fostering diverse subpopulations and mitigating the risk of premature convergence. In the intra-island optimization phase, a semi-supervised learning method is devised to fine-tune the surrogates. It not only facilitates data argumentation, but also incorporates the distribution information gathered during the search process to align the surrogates with the evolving local landscapes. Then, in the inter-island knowledge transfer phase, the algorithm incorporates an adaptive strategy that periodically transfers individual information and evaluates the transfer effectiveness in the new environment, facilitating global optimization efficacy. Experimental results demonstrate that our algorithm is competitive with state-of-the-art DDEAs on problems with up to 1000 dimensions, while also exhibiting decent parallelism and scalability. Our DSKT-DDEA is open-source and accessible at: https://github.com/LabGong/DSKT-DDEA.
Cancermorphic Computing Toward Multilevel Machine Intelligence
Authors: Rosalia Moreddu, Michael Levin
Date: 2025-03-17 00:00:00

Despite their potential to address crucial bottlenecks in computing architectures and contribute to the pool of biological inspiration for engineering, pathological biological mechanisms remain absent from computational theory. We hereby introduce the concept of cancer-inspired computing as a paradigm drawing from the adaptive, resilient, and evolutionary strategies of cancer, for designing computational systems capable of thriving in dynamic, adversarial or resource-constrained environments. Unlike known bioinspired approaches (e.g., evolutionary and neuromorphic architectures), cancer-inspired computing looks at emulating the uniqueness of cancer cells survival tactics, such as somatic mutation, metastasis, angiogenesis and immune evasion, as parallels to desirable features in computing architectures, for example decentralized propagation and resource optimization, to impact areas like fault tolerance and cybersecurity. While the chaotic growth of cancer is currently viewed as uncontrollable in biology, randomness-based algorithms are already being successfully demonstrated in enhancing the capabilities of other computing architectures, for example chaos computing integration. This vision focuses on the concepts of multilevel intelligence and context-driven mutation, and their potential to simultaneously overcome plasticity-limited neuromorphic approaches and the randomness of chaotic approaches. The introduction of this concept aims to generate interdisciplinary discussion to explore the potential of cancer-inspired mechanisms toward powerful and resilient artificial systems.
The Space Between: On Folding, Symmetries and Sampling
Authors: Michal Lewandowski, Bernhard Heinzl, Raphael Pisoni, Bernhard A. Moser
Date: 2025-03-11 00:00:00

Recent findings suggest that consecutive layers of neural networks with the ReLU activation function \emph{fold} the input space during the learning process. While many works hint at this phenomenon, an approach to quantify the folding was only recently proposed by means of a space folding measure based on Hamming distance in the ReLU activation space. We generalize this measure to a wider class of activation functions through introduction of equivalence classes of input data, analyse its mathematical and computational properties and come up with an efficient sampling strategy for its implementation. Moreover, it has been observed that space folding values increase with network depth when the generalization error is low, but decrease when the error increases. This underpins that learned symmetries in the data manifold (e.g., invariance under reflection) become visible in terms of space folds, contributing to the network's generalization capacity. Inspired by these findings, we outline a novel regularization scheme that encourages the network to seek solutions characterized by higher folding values.
A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling
Authors: Pouya Shaeri, Saud AlKhaled, Ariane Middel
Date: 2025-03-11 00:00:00
Tasks: Physics-informed machine learning

Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption, and urban planning. Mean Radiant Temperature ($T_{mrt}$) is a key parameter for evaluating outdoor thermal comfort, especially in urban environments where radiation dynamics significantly impact human thermal exposure. Traditional methods of estimating $T_{mrt}$ rely on field measurements and computational simulations, both of which are resource intensive. This study introduces a Physics-Informed Neural Network (PINN) approach that integrates shortwave and longwave radiation modeling with deep learning techniques. By leveraging a multimodal dataset that includes meteorological data, built environment characteristics, and fisheye image-derived shading information, our model enhances predictive accuracy while maintaining physical consistency. Our experimental results demonstrate that the proposed PINN framework outperforms conventional deep learning models, with the best-performing configurations achieving an RMSE of 3.50 and an $R^2$ of 0.88. This approach highlights the potential of physics-informed machine learning in bridging the gap between computational modeling and real-world applications, offering a scalable and interpretable solution for urban thermal comfort assessments.
($\boldsymbolθ_l, \boldsymbolθ_u$)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
Authors: Tingyang Wei, Jiao Liu, Abhishek Gupta, Puay Siew Tan, Yew-Soon Ong
Date: 2025-03-11 00:00:00

Multi-task optimization is typically characterized by a fixed and finite set of optimization tasks. The present paper relaxes this condition by considering a non-fixed and potentially infinite set of optimization tasks defined in a parameterized, continuous and bounded task space. We refer to this unique problem setting as parametric multi-task optimization (PMTO). Assuming the bounds of the task parameters to be ($\boldsymbol{\theta}_l$, $\boldsymbol{\theta}_u$), a novel ($\boldsymbol{\theta}_l$, $\boldsymbol{\theta}_u$)-PMTO algorithm is crafted to enable joint search over tasks and their solutions. This joint search is supported by two approximation models: (1) for mapping solutions to the objective spaces of all tasks, which provably accelerates convergence by acting as a conduit for inter-task knowledge transfers, and (2) for probabilistically mapping tasks to the solution space, which facilitates evolutionary exploration of under-explored regions of the task space. At the end of a full ($\boldsymbol{\theta}_l$, $\boldsymbol{\theta}_u$)-PMTO run, the acquired models enable rapid identification of optimized solutions for any task lying within the specified bounds. This outcome is validated on both synthetic test problems and practical case studies, with the significant real-world applicability of PMTO shown towards fast reconfiguration of robot controllers under changing task conditions. The potential of PMTO to vastly speedup the search for solutions to minimax optimization problems is also demonstrated through an example in robust engineering design.
Identification and estimation of treatment effects in a linear factor model with fixed number of time periods
Authors: Koki Fusejima, Takuya Ishihara
Date: 2025-03-27 00:00:00
Tasks: valid

This paper provides a new approach for identifying and estimating the Average Treatment Effect on the Treated under a linear factor model that allows for multiple time-varying unobservables. Unlike the majority of the literature on treatment effects in linear factor models, our approach does not require the number of pre-treatment periods to go to infinity to obtain a valid estimator. Our identification approach employs a certain nonlinear transformations of the time invariant observed covariates that are sufficiently correlated with the unobserved variables. This relevance condition can be checked with the available data on pre-treatment periods by validating the correlation of the transformed covariates and the pre-treatment outcomes. Based on our identification approach, we provide an asymptotically unbiased estimator of the effect of participating in the treatment when there is only one treated unit and the number of control units is large.
Speeding up Local Search for the Indicator-based Subset Selection Problem by a Candidate List Strategy
Authors: Keisuke Korogi, Ryoji Tanabe
Date: 2025-03-06 00:00:00

In evolutionary multi-objective optimization, the indicator-based subset selection problem involves finding a subset of points that maximizes a given quality indicator. Local search is an effective approach for obtaining a high-quality subset in this problem. However, local search requires high computational cost, especially as the size of the point set and the number of objectives increase. To address this issue, this paper proposes a candidate list strategy for local search in the indicator-based subset selection problem. In the proposed strategy, each point in a given point set has a candidate list. During search, each point is only eligible to swap with unselected points in its associated candidate list. This restriction drastically reduces the number of swaps at each iteration of local search. We consider two types of candidate lists: nearest neighbor and random neighbor lists. This paper investigates the effectiveness of the proposed candidate list strategy on various Pareto fronts. The results show that the proposed strategy with the nearest neighbor list can significantly speed up local search on continuous Pareto fronts without significantly compromising the subset quality. The results also show that the sequential use of the two lists can address the discontinuity of Pareto fronts.
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?
Authors: Hongyao Tang
Date: 2025-03-06 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL), Trajectory Modeling

Learning the optimal policy from a random network initialization is the theme of deep Reinforcement Learning (RL). As the scale of DRL training increases, treating DRL policy network weights as a new data modality and exploring the potential becomes appealing and possible. In this work, we focus on the policy learning path in deep RL, represented by the trajectory of network weights of historical policies, which reflects the evolvement of the policy learning process. Taking the idea of trajectory modeling with Transformer, we propose Transformer as Implicit Policy Learner (TIPL), which processes policy network weights in an autoregressive manner. We collect the policy learning path data by running independent RL training trials, with which we then train our TIPL model. In the experiments, we demonstrate that TIPL is able to fit the implicit dynamics of policy learning and perform the optimization of policy network by inference.
Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory
Authors: Niklas M. Melton, Leonardo Enzo Brito da Silva, Sasha Petrenko, Donald. C. Wunsch II
Date: 2025-03-05 00:00:00
Tasks: Clustering

This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities.
Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach
Authors: Václav Jirkovský, Jiří Kubalík, Petr Kadera, Arnd Schirrmann, Andreas Mitschke, Andreas Zindel
Date: 2025-03-05 00:00:00
Tasks: Management

This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use of the OWL ontology for data consistency and constraint management. The experimental validation demonstrates the effectiveness of the approach in both single and double sourcing scenarios. The proposed methodology, in general, can be applied to any industry case with complex manufacturing and supply chain challenges.
Securing the supply of graphite for batteries
Authors: Karan Bhuwalka, Hari Ramachandran, Swati Narasimhan, Adrian Yao, Julia Frohmann, Leopold Peiseler, William Chueh, Adam Boies, Steven J. Davis, Sally Benson
Date: 2025-03-27 00:00:00

The increasing demand for graphite in batteries has led to concerns around supply chain security. Currently, over 92% of global anode material is produced in China, posing a geopolitical risk for other countries reliant on graphite supply for domestic industries. This paper assesses the costs of producing battery-grade graphite (natural and synthetic) in the US and China using process-based cost models. We find that production costs in the US significantly exceed those in China due to higher capital intensity and input costs. Our analysis reveals that a majority of modeled projects in the US are not competitive at current market prices. We identify key cost drivers, including capital costs, economies of scale, and input material prices, and explore pathways to improve the competitiveness of US graphite production, such as supportive financing and process innovation directions. The analysis of conventional graphite production costs at scale also informs ceiling costs for alternative, promising pathways such as methane pyrolysis and catalytic graphitization. This study highlights the challenges and trade-offs in building a diversified graphite supply chain and informs policy and investment decisions.
Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
Authors: Da Wang, Yu Zhang, Kai Zhang, Junqing Li, Dengwang Li
Date: 2025-03-04 00:00:00
Tasks: Job Shop Scheduling, Scheduling

As the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need sensible energy-saving scheduling schemes to achieve the balance between improving production efficiency and reducing energy consumption. In energy-saving scheduling, reasonable machine states-switching is a key point to achieve expected goals, i.e., whether the machines need to switch speed between different operations, and whether the machines need to add extra setup time between different jobs. Regarding this matter, this work proposes a novel machine multi states-based energy saving flexible job scheduling problem (EFJSP-M), which simultaneously takes into account machine multi speeds and setup time. To address the proposed EFJSP-M, a kind of discrete differential evolution particle swarm optimization algorithm (D-DEPSO) is designed. In specific, D-DEPSO includes a hybrid initialization strategy to improve the initial population performance, an updating mechanism embedded with differential evolution operators to enhance population diversity, and a critical path variable neighborhood search strategy to expand the solution space. At last, based on datasets DPs and MKs, the experiment results compared with five state-of-the-art algorithms demonstrate the feasible of EFJSP-M and the superior of D-DEPSO.
Sustainable AI: Mathematical Foundations of Spiking Neural Networks
Authors: Adalbert Fono, Manjot Singh, Ernesto Araya, Philipp C. Petersen, Holger Boche, Gitta Kutyniok
Date: 2025-03-03 00:00:00
Tasks: Learning Theory

Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm.
Smart treaties: A path to binding agreements in international relations?
Authors: Niklas Valentin Lehmann
Date: 2025-03-27 00:00:00

Can we create binding agreements between nations? Recently, scholars have argued that blockchain technology enables us to do so. Given that this could greatly affect the anarchical world order implied by state sovereignty, this remarkable claim is investigated thoroughly. By focusing on the technical implementation of smart contracts between nations, this article finds that the potential to create binding agreements using blockchain technology is far more limited than recently suggested.
The evolving boundary of green technology
Authors: Nicolò Barbieri, Kerstin Hötte, Peter Persoon
Date: 2025-03-27 00:00:00
Tasks: Patent classification

Green patents are a key indicator to track technological efforts aimed at fighting climate change. Using an original dataset that merges different Patstat releases, we identify three mechanisms that may bias green patent statistics, potentially leading to contradictory findings. First, patent reclassifications due to updates in (green) classification codes result in an 9.2\% increase in the number of green patents when using the most recent classification structure. Second, delays in the adoption of the Cooperative Patent Classification (CPC) system introduce regional biases, as approximately 10\% of green patents from late-adopting countries remain undetected in less recent versions of the database. Third, we provide evidence that quality thresholds used to identify high-value inventions significantly shape observed trends in green patenting. Analyzing these mechanisms, our paper reveals that in many studies a substantial number of green patents is systematically overlooked, with the strongest effects observed for recent years and patents originating from Asian patent offices. These findings lead to relevant policy implications. Our results indicate not only that the global rate of green innovation has accelerated, but also that its epicenter has shifted, with an increasing share of green patents originating from emerging technological leaders, particularly in Asia.