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Search · four archives
26 papers · ranked by Valyu relevance
Pavel Trojovský, Mohammad Dehghani, Szymon Łukasik, Mikołaj Leszczuk + 1 more
'Szymon Szott'] Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is…
Mohammad Dehghani, Štěpán Hubálovský, Pavel Trojovský, Wojciech Kempa + 1 more
'Wojciech Kempa' 'Iwona Paprocka'] Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a…
Ewelina Węglarz‐Tomczak, Jakub M. Tomczak, A. E. Eiben, Stanley Brul
Models in systems biology are mathematical descriptions of biological processes that are used to answer questions and gain a better understanding of biological phenomena. Dynamic models represent the network through rates of the production and consumption for the individual species. The ordinary differential equations…
Mohammad Dehghani, Eva Trojovská, Pavel Trojovský, Om Parkash Malik + 1 more
'Huiling Chen'] This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while…
Sajjad Amiri Doumari, Hadi Givi, Mohammad Dehghani, Zeinab Montazeri + 3 more
'Victor Leiva' 'Josep M. Guerrero' 'Antonio M. Scarfone'] Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of…
Daniel Gaissmaier, Matthias van den Borg, Donato Fantauzzi, Timo Jacob
In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in…
Michael A. Lones
This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same…
Hassan A. Bashir, R.S. Neville
This material represents the opinion of the author only and does not necessarily represent the opinion of the School or the University. While every attempt has been made to ensure the accuracy of this publication, neither the author, the School or the University can accept liability for mistakes that may exist. Please…
Brianna Greenstein, Danielle Elsey, Geoffrey Hutchison
Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this work, we examine the general effects of a number of hyperparameters, such as population…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Mona Nasr, Omar Farouk, Ahmed Mohamedeen, Ali Elrafie + 2 more
'Marwan Bedeir' 'Ali Khaled'] Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent…
Jakub Otwinowski, Colin H. LaMont, Armita Nourmohammad
Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between classical population genetics of quantitative traits and evolutionary optimization, and formulate a new evolutionary algorithm. Optimization of a…
Dogan Corus, Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre
Understanding how the time-complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evolutionary computation. Most rigorous results were derived using a handful of key analytic techniques, including drift analysis. However…
Nicolas Heslot, Vitaliy Feoktistov
Calibration population design for genomic prediction has attracted a lot of interest in the plant and animal breeding literature. In this article we present an efficient optimization method to select a subset of preexisting individuals to phenotype. Application to the choice of maize hybrids to create and phenotype, to…
Learnmore Moyo, Nnamdi I. Nwulu, Uduakobong E. Ekpenyong
The Generator Maintenance Schedule model is formulated mathematically as a highly constrained combinatorial optimization problem and it is obligatory to implement a suitable optimization tool to determine the best feasible maintenance schedule. The maintenance schedule obtained has to meet a number of power system…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
José C. Pereira, Fernando G. Lobo
The Parameter-less Genetic Algorithm was first presented by Harik and Lobo in 1999 as an alternative to the usual trial-and-error method of finding, for each given problem, an acceptable set-up of the parameter values of the genetic algorithm. Since then, the same strategy has been successfully applied to create…
Steen Lysgaard, Paul C. Jennings, Jens Strabo Hummelshøj, Thomas Bligaard + 1 more
A machine learning (ML) model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in Genetic Algorithm (GA)-based approaches by using the ML model as a surrogate. This leads to a machine learning accelerated genetic algorithm (MLaGA) combining robust…
William E. Peterman
Understanding how landscape features affect functional connectivity among populations is a cornerstone of landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is largely a subjective process that explores a limited parameter space. ResistanceGA is a new R package…
Juan Luís Jiménez Laredo, Carlos M. Fernandes, J. J. Merelo, Christian Gagné
'Christian Gagné'] Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of…
D. Tarunika, Ashish Sharma
Multi-objective optimization problems (MOPs) demand algorithms that effectively balance convergence, diversity, and computational efficiency. To address this challenge, a novel Multi-Objective Human Evolutionary Optimization Algorithm (MOHEOA) is proposed, inspired by the dynamics of human societal evolution. MOHEOA…
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…
Ekaterina Noskova, Vladimir Ulyantsev, Stephen J. O’Brien, Pavel Dobrynin
During the last few years more and more genomic analysis of individuals of closely related species has appeared. The history of evolution and development of populations, so-called demographic history, is embedded into their genome and we can try to pull it out. Allele frequency spectrum or AFS, the distribution of…
Justin Villard, Murat Kılıç, Ursula Rothlisberger
Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this…
Yuta Sakurai, Yutaka Hori
Model-guided design has become a standard approach to engineering biomolecular circuits in current synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell-cell heterogeneity causes unexpected deviation of biocircuit behaviors from model…
Tong Liang, Braden A. W. Brinkman
Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a…