Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Zhenhao Shuai, Hongbo Liu, Zhaolin Wan, Wei–Jie Yu + 1 more
Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concerning both their scale and performance. In this study, we propose a self-adaptive neuroevolution (SANE) approach to automatically construct…
Szilárd Kovács, Csaba Budai, János Botzheim
In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends the Bacterial Memetic Algorithm by integrating Cultural Algorithms and co-evolutionary dynamics inspired by bacterial group behavior. This combination of…
Sadeer Fadhil, Hegazy Zaher, Naglaa Ragaa, Eman Oun
The differential evolution algorithm is one of the promising natural inspired population-based metaheuristic algorithms that attracted the attention of researchers in the recent years. This paper presents a new mutation strategy called DE/current-to-best/2 that presents a new mutated vector based on utilizing the…
Rongjie Liao, Junhao Qiu, Xin Chen, Xiaoping Li
Customized static operator design has enabled widespread application of Evolutionary Algorithms (EAs), but their search performance is transient during iterations and prone to degradation. Dynamic operators aim to address this but typically rely on predefined designs and localized parameter control during the search…
Furong Ye, Frank Neumann, Jacob de Nobel, Aneta Neumann + 1 more
'Thomas Bäck'] Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algorithms for singleobjective optimization, little is known about how self-adaptation influences multi-objective evolutionary…
Halima Bouzidi, Smaïl Niar, Hamza Ouarnoughi, El‐Ghazali Talbi
Architecture Search Authors: ['Halima Bouzidi' 'Smaïl Niar' 'Hamza Ouarnoughi' 'El‐Ghazali Talbi'] Abstract—Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments of Internet of Things (IoT)…
Xuening Wu, Xinhang Zhang, Yanlan Kang, Qianya Xu + 2 more
Despite advances such as AlphaFold and modern generative AI models, current drug discovery pipelines lack mechanisms to refine both molecules and the pipelines themselves, limiting their ability to achieve autonomous and reliable self-improvement. To address this, we present the Darwin–Gödel Drug Discovery Machine…
Alok Kumar Shukla, Shubhra Dwivedi, Deepak Singh, Sunil Kumar Singh + 2 more
'Diwakar Tripathi' 'Ram Kishan Dewangan'] Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital…
Akarsh Kumar, Bo Liu, Risto Miikkulainen, Peter Stone
Evolutionary algorithms are sensitive to the mutation rate (MR); no single value of this parameter works well across domains. Selfadaptive MR approaches have been proposed but they tend to be brittle: Sometimes they decay the MR to zero, thus halting evolution. To make self-adaptive MR robust, this paper introduces the…
Emanuel Vega, José Lemus-Romani, Ricardo Soto, Broderick Crawford + 4 more
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years…
Brindha Subburaj, S. Miruna Joe Amali
Simulated by nature’s evolution, numerous evolutionary algorithms had been proposed. These algorithms perform better for a particular problem domain and extensive parameter fine tuning and adaptations are required in optimizing problems of varied domain. This paper aims to develop robust and self-adaptive memetic…
Pawan Mishra, Musrrat Ali, Pooja, Safiqul Islam + 1 more
Differential evolution (DE) stands out as a prominent algorithm for addressing global optimization challenges. The efficacy of DE hinges crucially upon its mutation operation, which serves as a pivotal mechanism in generating diverse and high-quality solutions. This article explores various mutation operations aimed at…
Motoaki Hiraga, Masahiro Komura, Akiharu Miyamoto, Daichi Morimoto + 2 more
'Kazuhiro Ohkura' 'Ziqiang Zeng'] Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of neural networks. In…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…
Christopher L. Buckley, Tim Lewens, Mike Levin, Beren Millidge + 2 more
Evolution by natural selection is believed to be the only possible source of spontaneous adaptive organisation in the natural world. This places strict limits on the kinds of systems that can exhibit adaptation spontaneously, i.e. without design. Physical systems can show some properties relevant to adaptation without…
Vincent Cicirello
When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope to include evolutionary computation. This paper concerns the evolutionary algorithms that…
P. Carvalho, Jessica Mégane, Nuno Lourenço, Penousal Machado
This work proposes Adaptive Facilitated Mutation, a selfadaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our…
Wenli Xu, Chunrong Wu, Qinglan Peng, Jia Lee + 2 more
'Shuji Kawasaki'] Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda
Differentiable programming frameworks like PyTorch and JAX revolutionized biological modeling. A foremost merit is that multiple components programmed separately can be put together so that the parameters are jointly optimized. Despite its proven value in agricultural applications, existing breeding simulators are…
Lillian T. Tatka, Lucian P. Smith, Herbert M. Sauro
Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain…
Omer Markovitch, Juntian Wu, Otto Sijbren
Copying information is vital for life's propagation. Current life forms maintain a low error rate in replication using complex machinery to prevent and correct errors. However, primitive life had to deal with higher error rates, limiting its ability to evolve. Discovering mechanisms to reduce errors would alleviate…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…