15 papers · ranked by Valyu relevance
He Li, Naiyu Shi
In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions…
Junyu Zhang, Pengyuan Qi, Jike Wang, S. Svensson
A universal optimization simulation method based on a multi-objective genetic algorithm is introduced; this is the first attempt to optimize the elements of a beamline using this method.
Maxinder S Kanwal, Avinash S Ramesh, Lauren A Huang
Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural…
Tobias B. Alter, Lars M. Blank, Birgitta E. Ebert
To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering…
Ahmed Redha Mahlous, Houssam Mahlous, Yilun Shang
Universities face a constant challenge when distributing students and allocating them to their required classes, especially for a large mass of students. Generating feasible timetables is a strenuous task that requires plenty of resources, which makes it impractical to take student preferences into consideration during…
Tiantian Mi
Laboratory equipment planning is a very important task in modern enterprise management. Laboratory equipment planning by computer algorithm is a very complex NP-hard combinatorial optimization problem, so it is impossible to find an accurate algorithm in polynomial time. In this study, an improved genetic algorithm is…
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…
Steve O'Hagan, Joshua Knowles, Douglas B. Kell, Josh Bongard
Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this…
Hygor Piaget M. Melo, Alexander Franks, André A. Moreira, Daniel Diermeier + 3 more
'Daniel Diermeier' 'José S. Andrade Jr' 'Luís A. N. u. n. e. s. Amaral' 'Maria Anisimova'] Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they…
Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore
Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm…
Sizhe Yuen, Thomas H. G. Ezard, Adam J. Sobey
Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the…
Jaya Shankar Tumuluru, Richard McCulloch, Wijitha Senadeera
Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article…
Alexander Lalejini, Emily Dolson, Anya E Vostinar, Luis Zaman + 2 more
'C Brandon Ogbunugafor' 'Christian R Landry'] Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses…
Alison A Motsinger-Reif, Sushamna Deodhar, Stacey J Winham, Nicholas E Hardison
'Nicholas E Hardison'] Background A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions.…
Stephen D Turner, Scott M Dudek, Marylyn D Ritchie
Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene…