Results 311 to 320 of about 7,762,731 (372)

Development and validation of a genetic algorithm for flexible docking.

Journal of Molecular Biology, 1997
Prediction of small molecule binding modes to macromolecules of known three-dimensional structure is a problem of paramount importance in rational drug design (the "docking" problem).
G. Jones, P. Willett, R. Glen, Andrew R. Leach, Robin Taylor   +4 more
semanticscholar   +1 more source

Genetic algorithms for genetic mapping [PDF]

, 1998
Constructing genetic maps is a prerequisite for most in-depth genetic studies of an organism. The problem of constructing reliable genetic maps for any organism can be considered as a complex optimization problem with both discrete and continuous parameters. This paper shows how genetic algorithms can been used to tackle this problem on simple pedigree.
Gaspin, Christine, Schiex, Thomas
openaire   +3 more sources

Genetic Algorithm- A Literature Review

International Conference Machine Learning, Big Data, Cloud and Parallel Computing, 2019
Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development ...
Annu Lambora, Kunal Gupta, K. Chopra
semanticscholar   +1 more source

A niched Pareto genetic algorithm for multiobjective optimization

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 1994
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by ...
Jeffrey D. Horn, Nicholas Nafpliotis, D. Goldberg   +2 more
semanticscholar   +1 more source

Genetic algorithm solution to unit commitment problem

IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, 2016
In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost.
Hatim S. Madraswala, A. Deshpande
semanticscholar   +1 more source

Comparative review of selection techniques in genetic algorithm

International Conference on Futuristic Trends on Computational Analysis and Knowledge Management, 2015
This paper compares various selection techniques used in Genetic Algorithm. Genetic algorithms are optimization search algorithms that maximize or minimizes given functions.
Anupriya Shukla, Hari Mohan Pandey, D. Mehrotra   +2 more
semanticscholar   +1 more source

Genetic algorithms in chemistry

Journal of Chromatography A, 2007
Genetic algorithms (GAs) are a quite recent technique of optimization, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest." The application of genetic algorithms to complex problems usually produces much better results than those obtained by the standard techniques.
openaire   +5 more sources

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

IEEE Transactions on Industrial Informatics, 2013
The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning.
Vincent Roberge, M. Tarbouchi, G. Labonté   +2 more
semanticscholar   +1 more source

Optimization of Genetic Algorithms by Genetic Algorithms

1993
This paper presents an approach to determine the optimal Genetic Algorithm (GA), i.e. the most preferable type of genetic operators and their parameter settings, for a given problem. The basic idea is to consider the search for the best GA as an optimization problem and use another GA to solve it. As a consequence, a primary GA operates on a population
Michael Härtfelder, Bernd Freisleben
openaire   +2 more sources

Introduction to genetic algorithms

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2007
The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start "at the beginning," describing first a "classical" genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance ...
openaire   +2 more sources