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Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling
For an efficient upconvert of the Pareto front resolution by utilizing a known candidate solution set, this paper proposed an algorithm that built...
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A Pareto front estimation-based constrained multi-objective evolutionary algorithm
The balance of convergence, diversity, and feasibility plays a pivotal role in constrained multi-objective optimization problems. To address this...
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PROUD: PaRetO-gUided diffusion model for multi-objective generation
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent...
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Pareto Front Estimation Using Unit Hyperplane
This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front...
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Pareto-efficient designs for multi- and mixed-level supersaturated designs
Supersaturated designs are used in science and engineering to efficiently explore a large number of factors with a limited number of runs. It is not...
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Privacy-Preserving Split Learning via Pareto Optimal Search
With the rapid development of deep learning, it has become a trend for clients to perform split learning with an untrusted cloud server. The models...
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Tuning parameters of Apache Spark with Gauss–Pareto-based multi-objective optimization
When there is a need to make an ultimate decision about the unique features of big data platforms, one should note that they have configurable...
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Parameter optimization of chaotic system using Pareto-based triple objective artificial bee colony algorithm
Chaotic map is a kind of discrete chaotic system. The existing chaotic maps suffer from optimal parameters in terms of chaos measurements. In this...
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A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization
In the last decade, it is widely known that the Pareto dominance-based evolutionary algorithms (EAs) are unable to deal with many-objective...
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Mining Pareto-optimal counterfactual antecedents with a branch-and-bound model-agnostic algorithm
Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating...
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RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving
Multi-objective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a certain trade-off among objectives....
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On Fast Multi-objective Optimization of Antenna Structures Using Pareto Front Triangulation and Inverse Surrogates
Design of contemporary antenna systems is a challenging endeavor, where conceptual developments and initial parametric studies, interleaved with...
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A model-based many-objective evolutionary algorithm with multiple reference vectors
In order to estimate the Pareto front, most of the existing evolutionary algorithms apply the discovery of non-dominated solutions in search space,...
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Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets
The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good...
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Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm
With the proliferation of IoT devices and the increasing popularity of location-oriented services in cyber-physical-social systems, the cognitive...
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A novel feature selection approach with Pareto optimality for multi-label data
Multi-label learning has widely applied in machine learning and data mining. The purpose of feature selection is to select an approximately optimal...
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Preprocessing Matters: Automated Pipeline Selection for Fair Classification
Improving fairness by manipulating the preprocessing stages of classification pipelines is an active area of research, closely related to AutoML. We...
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An adaptive boundary-based selection many-objective evolutionary algorithm with density estimation
Many-objective evolutionary algorithms often struggle to strike a balance between convergence and diversity when solving many-objective optimization...
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An Improved NSGA-II Algorithm with Markov Networks
NSGA-II algorithm is one of the most representative multi-objective Evolutionary Algorithms. With the help of elite preserving strategy and fast...
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A many-objective evolutionary algorithm with adaptive convergence calculation
Since different reference points are crucial for calculating convergence, we design a many-objective evolutionary algorithm with an adaptive...
