6 edition of Genetic Algorithms in Optimisation, Simulation and Modelling, (Frontiers in Artificial Intelligence and Applications , Vol 23) (Frontiers in Artificial Intelligence and Applications , Vol 23) found in the catalog.
January 1, 1994
by IOS Press
Written in English
|Contributions||J. Stender (Editor), E. Hillebrand (Editor), J. Kingdon (Editor)|
|The Physical Object|
|Number of Pages||270|
Genetic Algorithm Applications to Optimization Modeling: /ch Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in. The genetic algorithms performance is largely influenced by crossover and mutation operators. The block diagram representation of genetic algorithms (GAs) is shown in Fig 3. Encoding Technique in Genetic Algorithms (GAs) Encoding techniques in genetic algorithms (GAs) are problem specific, which transforms the problem solution into chromosomes.
In this paper, a simulation based optimization method is introduced. This method uses the technique of coupling industrial simulation software with a multi objective optimizer based on a genetic algorithm. This coupling is used to optimize the performances of a simulation model representing a railway maintenance facility by choosing the best queues' scheduling policy. This is a very useful book. It starts by introducing very important and interesting techniques in optimisation i.e. genetic algorithms, simulated annealing, tabu search and neural networks. It continues by giving examples of how the techniques have been applied in various case s: 3.
Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with passenger trips per day. Downloadable (with restrictions)! The objective of this research is to develop a genetic algorithm (GA)-based optimisation approach for a multi-echelon closed-loop inventory system of items, which are repairable in nature. In the context of the passenger transportation industry, engineering aggregates like engines, alternators, axles and tyres are representative examples of such systems.
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The book presents a collection of chapters dealing with a wide selection of topics concerning different applications of modeling.
It includes modeling, simulation and optimization applications in the areas of medical care systems, genetics, business, ethics and linguistics, applying very sophisticated methods. Algorithms, 3-D modeling, virtual reality, multi objective optimization, Cited by: 4.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).
Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators Genetic Algorithms in Optimisation as mutation, crossover and selection.
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Genetic Algorithms: a Tool for Modelling, Simulation, and Optimization of Complex Systems. Article (PDF Available) in Cybernetics and Systems 29(7) October with Reads. Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution.
They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in by: The paper presents an attempt to apply genetic algorithms (GAs) to the problem of optimising an existing simulation model.
A simple real-coded GA is presented and used to change the simulation model parameters. With each new parameter set proposed, a simulation run is performed. book optimization (SO) methods are optimization methods that generate and use random stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints.
Stochastic optimization methods also include methods with random iterates. TY - CONF AU - Jungang Luo AU - Xiao Zhang AU - Xuan Zhang PY - /05 DA - /05 TI - Multi-Objective Calibration of Nonlinear Muskingum Model Using Non-Dominated Sorting Genetic Algorithm-II BT - International Conference on Applied Mathematics, Simulation and Modelling PB - Atlantis Press SP - EP - SN - X UR - https.
21 hours ago The construction and expansion of steam cracking plants and feedstock diversification have resulted in a significant demand for the numerical simulation and optimization of models to achieve molecular refining and intelligent manufacturing.
However, the existing models cannot be widely applied in industrial practice because of the high computational expense, time-consumption, and data. GENETIC ALGORITHMS: A TOOL FOR MODELLING, SIMULATION, AND OPTIMIZATION OF COMPLEX SYSTEMS. Cybernetics and Systems: Vol.
29, No. 7, pp. Computing Tools for Modeling, Optimization and Simulation reflects the need for preserving the marriage between operations research and computing in order to create more efficient and powerful software tools in the years ahead. The 17 papers included in this volume were carefully selected to cover a wide range of topics related to the interface between operations research and computer science.
The proposed system could be divided into two main modules, the optimisation model and the production floor model. Indeed, the complexity of this scenario demands a hybrid model which involves a combination of an optimisation model (genetic algorithm model) and a production simulation model (discrete event simulation) with a robust link.
Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II.
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A comprehensive guide to a powerful new analytical tool by two of its foremost innovators The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking.
Modeling, Analysis, Simulation and Design Optimization (Genetic Algorithm) of dc-dc Converter for Uninterruptible Power Supply Applications Abstract: This paper presents the computer modeling, analysis, simulation and design optimization of an efficient dc-dc converter with synchronous rectification, which is important in battery charge.
The computational time required for the solution of genetic algorithm groundwater management models increases with the complexity of the problem. The speedup attainable by solving genetic algorithm problems on massively parallel computers is significant for problems where the simulation time required to complete each generation is high.
Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NO x emissions constraints.
The optimized configuration from ML-GA was found to be very close to that. Solving problems in maths by optimization technique using GA | Explore the latest full-text research PDFs, articles, conference papers, preprints and more on GENETIC ALGORITHM.
Find methods. Genetic Algorithms in Molecular Modeling is the first book available on the use of genetic algorithms in molecular design. This volume marks the beginning of an ew series of books, Principles in Qsar and Drug Design, which will be an indispensible reference for students and professionals involved in medicinal chemistry, pharmacology, (eco)toxicology, and agrochemistry.
In this paper we present a general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem.
When addressing such problems, genetic algorithms typically have difficulty maintaining feasibility from parent to offspring. That is, a genetic algorithm is used in three statistical optimization problems which are generally solved by numerical methods. The first problem involves the estimation of component mixture weights in a finite mixture model with known component density functions.To further incorporate resource optimization into construction planning, various genetic algorithms (GA)-optimized simulation models are integrated with commonly used project management software.
Accordingly, these models are activated from within the scheduling software to optimize the plan.Genetic algorithms in optimisation, simulation and modelling.