Papers Evolutionary Algorithms
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Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). T…
Evolutionary AlgorithmsA Tool for the Procedural Generation of Shaders using Interactive Evolutionary Algorithms
We present a tool for exploring the design space of shaders using an interactive evolutionary algorithm integrated with the Unity editor, a well-known commercial tool for video game development. Our framework leverages t…
Evolutionary AlgorithmsUnityA Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution
Computational cost in metaheuristics such as Evolutionary Algorithms (EAs) is often a major concern, particularly with their ability to scale. In data-based training, traditional EAs typically use a significant portion, …
Computational EfficiencyEvolutionary AlgorithmsSymbolic RegressionMultimodal Optimization with k-Cluster Big Bang-Big Crunch Algorithm and Postprocessing Methods for Identification and Quantification of Optima
Multimodal optimization is often encountered in engineering problems, especially when different and alternative solutions are sought. Evolutionary algorithms can efficiently tackle multimodal optimization thanks to their…
Evolutionary AlgorithmsA Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation
In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of …
Evolutionary Algorithmsfeature selectionOutlier DetectionDecomposing Hard SAT Instances with Metaheuristic Optimization
In the article, within the framework of the Boolean Satisfiability problem (SAT), the problem of estimating the hardness of specific Boolean formulas w.r.t. a specific complete SAT solving algorithm is considered. Based …
Evolutionary AlgorithmsMetaheuristic OptimizationSingle and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
The landscapes of real-world optimization problems can vary strongly depending on the application. In engineering design optimization, objective functions and constraints are often derived from governing equations, resul…
Evolutionary AlgorithmsMToP: A MATLAB Optimization Platform for Evolutionary Multitasking
Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging …
Evolutionary AlgorithmsTransfer LearningPre-Evolved Model for Complex Multi-objective Optimization Problems
Multi-objective optimization problems (MOPs) necessitate the simultaneous optimization of multiple objectives. Numerous studies have demonstrated that evolutionary computation is a promising paradigm for solving complex …
Evolutionary AlgorithmsSynergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutionary methods designed to generate a set …
Continuous ControlDiversityEvolutionary Algorithmsreinforcement-learning+1The Conquest of Quantum Genetic Algorithms: The Adventure to Cross the Valley of Death
In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However,…
Evolutionary AlgorithmsDARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence
We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning for efficiently training and evolving populations of UNIMAL agents. Our approach utilizes Proximal Policy Opti…
CPUDiversityEvolutionary AlgorithmsGPU+2Pointer Networks Trained Better via Evolutionary Algorithms
Pointer Network (PtrNet) is a specific neural network for solving Combinatorial Optimization Problems (COPs). While PtrNets offer real-time feed-forward inference for complex COPs instances, its quality of the results te…
Combinatorial OptimizationEvolutionary AlgorithmsCombining Kernelized Autoencoding and Centroid Prediction for Dynamic Multi-objective Optimization
Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change. This paper proposes a unified paradigm, whi…
DiversityEvolutionary AlgorithmsA Survey and Analysis of Evolutionary Operators for Permutations
There are many combinatorial optimization problems whose solutions are best represented by permutations. The classic traveling salesperson seeks an optimal ordering over a set of cities. Scheduling problems often seek op…
Combinatorial OptimizationEvolutionary AlgorithmsSchedulingSurveyDirect Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit
Optimization problems find widespread use in both single-objective and multi-objective scenarios. In practical applications, users aspire for solutions that converge to the region of interest (ROI) along the Pareto front…
Evolutionary AlgorithmsProtein Structure PredictionCoevolution of Neural Architectures and Features for Stock Market Forecasting: A Multi-objective Decision Perspective
In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to i…
Evolutionary Algorithmsfeature selectionLook into the Mirror: Evolving Self-Dual Bent Boolean Functions
Bent Boolean functions are important objects in cryptography and coding theory, and there are several general approaches for constructing such functions. Metaheuristics proved to be a strong choice as they can provide ma…
Evolutionary AlgorithmsA New Angle: On Evolving Rotation Symmetric Boolean Functions
Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions. At the same time, the functions in this class can have excellent…
Evolutionary AlgorithmsFast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels
The fitness level method is an easy-to-use tool for estimating the hitting time of elitist evolutionary algorithms. Recently, linear lower and upper bounds by fitness levels have been constructed. But these bounds requir…
Evolutionary Algorithms