21 research outputs found
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Federated Learning (FL) is a decentralized machine learning paradigm that
enables collaborative model training across dispersed nodes without having to
force individual nodes to share data. However, its broad adoption is hindered
by the high communication costs of transmitting a large number of model
parameters. This paper presents EvoFed, a novel approach that integrates
Evolutionary Strategies (ES) with FL to address these challenges. EvoFed
employs a concept of 'fitness-based information sharing', deviating
significantly from the conventional model-based FL. Rather than exchanging the
actual updated model parameters, each node transmits a distance-based
similarity measure between the locally updated model and each member of the
noise-perturbed model population. Each node, as well as the server, generates
an identical population set of perturbed models in a completely synchronized
fashion using the same random seeds. With properly chosen noise variance and
population size, perturbed models can be combined to closely reflect the actual
model updated using the local dataset, allowing the transmitted similarity
measures (or fitness values) to carry nearly the complete information about the
model parameters. As the population size is typically much smaller than the
number of model parameters, the savings in communication load is large. The
server aggregates these fitness values and is able to update the global model.
This global fitness vector is then disseminated back to the nodes, each of
which applies the same update to be synchronized to the global model. Our
analysis shows that EvoFed converges, and our experimental results validate
that at the cost of increased local processing loads, EvoFed achieves
performance comparable to FedAvg while reducing overall communication
requirements drastically in various practical settings
Computational and Exploratory Landscape Analysis of the GKLS Generator
The GKLS generator is one of the most used testbeds for benchmarking global
optimization algorithms. In this paper, we conduct both a computational
analysis and the Exploratory Landscape Analysis (ELA) of the GKLS generator. We
utilize both canonically used and newly generated classes of GKLS-generated
problems and show their use in benchmarking three state-of-the-art methods
(from evolutionary and deterministic communities) in dimensions 5 and 10. We
show that the GKLS generator produces ``needle in a haystack'' type problems
that become extremely difficult to optimize in higher dimensions. Furthermore,
we conduct the ELA on the GKLS generator and then compare it to the ELA of two
other widely used benchmark sets (BBOB and CEC 2014), and discuss the
meaningfulness of the results
Hardest Monotone Functions for Evolutionary Algorithms
The study of hardest and easiest fitness landscapes is an active area of
research. Recently, Kaufmann, Larcher, Lengler and Zou conjectured that for the
self-adjusting -EA, Adversarial Dynamic BinVal (ADBV) is the
hardest dynamic monotone function to optimize. We introduce the function
Switching Dynamic BinVal (SDBV) which coincides with ADBV whenever the number
of remaining zeros in the search point is strictly less than , where
denotes the dimension of the search space. We show, using a combinatorial
argument, that for the -EA with any mutation rate , SDBV is
drift-minimizing among the class of dynamic monotone functions. Our
construction provides the first explicit example of an instance of the
partially-ordered evolutionary algorithm (PO-EA) model with parameterized
pessimism introduced by Colin, Doerr and F\'erey, building on work of Jansen.
We further show that the -EA optimizes SDBV in
generations. Our simulations demonstrate matching runtimes for both static and
self-adjusting and -EA. We further show, using an
example of fixed dimension, that drift-minimization does not equal maximal
runtime
Understanding Trade-offs in Stellarator Design with Multi-objective Optimization
In designing stellarators, any design decision ultimately comes with a
trade-off. Improvements in particle confinement, for instance, may increase the
burden on engineers to build more complex coils, and the tightening of
financial constraints may simplify the design and worsen some aspects of
transport. Understanding trade-offs in stellarator designs is critical in
designing high performance devices that satisfy the multitude of physical,
engineering, and financial criteria. In this study we show how multi-objective
optimization (MOO) can be used to investigate trade-offs and develop insight
into the role of design parameters. We discuss the basics of MOO, as well as
practical solution methods for solving MOO problems. We apply these methods to
bring insight into the selection of two common design parameters: the aspect
ratio of an ideal magnetohydrodynamic equilibrium, and the total length of the
electromagnetic coils
Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms
Linkage learning techniques are employed to discover dependencies between problem variables. This knowledge can then be leveraged in an Evolutionary Algorithm (EA) to improve the optimization process. Of particular interest is the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) family, which has been shown to exploit linkage effectively. Recently, Empirical Linkage Learning (ELL) techniques were proposed for binary-encoded problems. While these techniques are computationally expensive, they have the benefit of never reporting spurious dependencies (false linkages), i.e., marking two independent variables as being dependent. However, previous research shows that despite this property, for some problems, it is more suitable to employ more commonly-used Statistical-based Linkage Learning (SLL) techniques. Therefore, we propose to use both ELL and SLL in the form of Hybrid Linkage Learning (HLL). We also propose (for the first time) a variant of ELL for permutation problems. Using a wide range of problems and different GOMEA variants, we find that also for permutation problems, in some cases, ELL is more advantageous to use while SLL is more advantageous in other cases. However, we also find that employing the proposed HLL leads to results that are better or equal than the results obtained with SLL for all the considered problems
An agent-based model of hierarchic genetic search
AbstractAn effective exploration of the large search space by single population genetic-based metaheuristics may be a very time consuming and complex process, especially in the case of dynamic changes in the system states. Speeding up the search process by the metaheuristic parallelisation must have a significant negative impact on the search accuracy.There is still a lack of complete formal models for parallel genetic and evolutionary techniques, which might support the parameter setting and improve the whole (often very complex) structure management.In this paper, we define a mathematical model of Hierarchical Genetic Search (HGS) based on the genetic multi-agent system paradigm. The model has a decentralised population management mechanism and the relationship among the parallel genetic processes has a multi-level tree structure. Each process in this tree is Markov-type and the conditions of the commutation of the Markovian kernels in HGS branches are formulated
Explainable Predictive Maintenance
Explainable Artificial Intelligence (XAI) fills the role of a critical
interface fostering interactions between sophisticated intelligent systems and
diverse individuals, including data scientists, domain experts, end-users, and
more. It aids in deciphering the intricate internal mechanisms of ``black box''
Machine Learning (ML), rendering the reasons behind their decisions more
understandable. However, current research in XAI primarily focuses on two
aspects; ways to facilitate user trust, or to debug and refine the ML model.
The majority of it falls short of recognising the diverse types of explanations
needed in broader contexts, as different users and varied application areas
necessitate solutions tailored to their specific needs.
One such domain is Predictive Maintenance (PdM), an exploding area of
research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights
the gap between existing XAI methodologies and the specific requirements for
explanations within industrial applications, particularly the Predictive
Maintenance field. Despite explainability's crucial role, this subject remains
a relatively under-explored area, making this paper a pioneering attempt to
bring relevant challenges to the research community's attention. We provide an
overview of predictive maintenance tasks and accentuate the need and varying
purposes for corresponding explanations. We then list and describe XAI
techniques commonly employed in the literature, discussing their suitability
for PdM tasks. Finally, to make the ideas and claims more concrete, we
demonstrate XAI applied in four specific industrial use cases: commercial
vehicles, metro trains, steel plants, and wind farms, spotlighting areas
requiring further research.Comment: 51 pages, 9 figure
Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows
An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel way to exploit the benefits of each method. The local search component uses a novel neighbourhood operator. A streamlined implementation of large neighbourhood search is used to achieve an effective balance between intensification and diversification. The adaptive ejection chain component perturbs the solution and uses increased or decreased computation time according to the progress of the search. While the local search and large neighbourhood search focus on minimising travel distance, the adaptive ejection chain seeks to reduce the number of routes. The proposed algorithm design results in an effective and fast solution method that finds a large number of new best known solutions on a well-known benchmark data set. Experiments are also performed to analyse the benefits of the components and heuristics and their combined use in order to achieve a better understanding of how to better tackle the subject problem