Papers Evolutionary Algorithms
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The Compact Genetic Algorithm Struggles on Cliff Functions
The compact genetic algorithm (cGA) is an non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In …
Evolutionary AlgorithmsDistributed Evolution Strategies for Black-box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a d…
Evolutionary AlgorithmsStochastic OptimizationReproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems
Dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purp…
Evolutionary AlgorithmsImproving LSHADE by means of a pre-screening mechanism
Evolutionary algorithms have proven to be highly effective in continuous optimization, especially when numerous fitness function evaluations (FFEs) are possible. In certain cases, however, an expensive optimization appro…
Evolutionary AlgorithmsAutomatic inference of fault tree models via multi-objective evolutionary algorithms
Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems. Traditionally, fault tree (FT) models are buil…
Decision MakingEvolutionary AlgorithmsManagementNovelty Driven Evolutionary Neural Architecture Search
Evolutionary algorithms (EA) based neural architecture search (NAS) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can be reduced by using a supernet for estima…
Evolutionary AlgorithmsNeural Architecture SearchEvolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
Evolutionary algorithms are wildly used in unmanned aerial vehicle path planning for their flexibility and effectiveness. Nevertheless, they are so sensitive to the change of environment that can't adapt to all scenarios…
Evolutionary AlgorithmsEfficiently Evolving Swarm Behaviors Using Grammatical Evolution With PPA-style Behavior Trees
Evolving swarm behaviors with artificial agents is computationally expensive and challenging. Because reward structures are often sparse in swarm problems, only a few simulations among hundreds evolve successful swarm be…
Evolutionary AlgorithmsSurrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system
Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs for these systems' mathematical formulat…
BIG-bench Machine LearningComputational EfficiencyEvolutionary AlgorithmsRank-based Non-dominated Sorting
Non-dominated sorting is a computational bottleneck in Pareto-based multi-objective evolutionary algorithms (MOEAs) due to the runtime-intensive comparison operations involved in establishing dominance relationships betw…
Evolutionary AlgorithmsFocused Jump-and-Repair Constraint Handling for Fixed-Parameter Tractable Graph Problems Closed Under Induced Subgraphs
Repair operators are often used for constraint handling in constrained combinatorial optimization. We investigate the (1+1)~EA equipped with a tailored jump-and-repair operation that can be used to probabilistically repa…
Combinatorial OptimizationEvolutionary AlgorithmsIntelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies
Machine learning driven trading strategies have garnered a lot of interest over the past few years. There is, however, limited consensus on the ideal approach for the development of such trading strategies. Further, most…
Evolutionary AlgorithmsAre Evolutionary Algorithms Safe Optimizers?
We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life. Such problems…
Evolutionary AlgorithmsRunning Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool. However, the theoretical foundation of multi-objective EAs (MOEAs), especially th…
Evolutionary AlgorithmsNon-Elitist Selection Can Improve the Performance of Irace
Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obt…
Bayesian OptimizationEvolutionary AlgorithmsAutomated Formulaic Alpha Generation for Quantitative Investing using Evolutionary Algorithms
A cosmic ray consists of mostly highly energetic protons that emanate from the sun, the Milky Way and distant galaxies. By colliding with particles in our atmosphere they trigger a chain reaction that leads to so calle…
Evolutionary AlgorithmsSolving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can capture the exploitable structure of a proble…
Evolutionary AlgorithmsPareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts
According to the published papers and books since the turn of the century, Pareto optimization is the dominating assessment method for multi-objective nonlinear optimization problems treated by population-based optimizer…
Evolutionary AlgorithmsNeural Architecture Search using Progressive Evolution
Vanilla neural architecture search using evolutionary algorithms (EA) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can be reduced by using a supernet to estim…
Evolutionary AlgorithmsNeural Architecture SearchAn Instance Space Analysis of Constrained Multi-Objective Optimization Problems
Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by t…
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