Spelling suggestions: "subject:"enetic algorithm"" "subject:"enetic dalgorithm""
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Prospects for applying speaker verification to unattended secure bankingHannah, Malcolm Ian January 1996 (has links)
No description available.

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Ambulance Optimization AllocationNasiri, Faranak 01 August 2014 (has links)
Facility Location problem refers to placing facilities (mostly vehicles) in appropriate locations to yield the best coverage with respect to other important factors which are specific to the problem. For instance in a fire station some of the important factors are traffic time, distribution of stations, time of the service and so on. Furthermore, budget limitation, time constraints and the great importance of the operation, make the optimum allocation very complex. In the past few years, several research in this area have been done to help managers by designing some effective algorithm to allocating facilities in the best way possible. Most early proposed models were focused on static and deterministic methods. In static models, once a facility assigns to a location, it will not relocate anymore. Although these methods could be utilized in some simple settings, there are so many factors in real world that make a static model of limited application. The demands may change over time or facilities may be dropped or added. In these cases a more flexible model is desirable, thus dynamic models are proposed to be used in such cases. Facilities can be located and relocated based on the situations. More recently, dynamic models became more popular but there were still many aspects of facility allocation problems which were challenging and would require more complex solutions. The importance of facility location problem becomes significantly more relevant when it relates to hospitals and emergency responders. Even one second of improvement in response time is important in this area. For this reason, we selected ambulance facility allocation problem as a case study to analyze this problem domain. Much research has been done on ambulances allocation. We will review some of these models and their advantages and disadvantages. One of the best model in this areas introduced by Rajagopalan. In this work, his model is analyzed and its major drawback is addressed by applying some modifications to its methodology. Genetic Algorithm is utilized in this study as a heuristic method to solve the allocation model.

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Truss topology optimization using an improved speciesconserving genetic algorithmLi, JianPing 06 February 2014 (has links)
Yes / The aim of this article is to apply and improve the speciesconserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A species is defined as a group of individuals with similar characteristics and is dominated by its species seed. The solutions of an optimization problem will be selected from the found species. To improve the accuracy of solutions, a species mutation technique is introduced to improve the fitness of the found species seeds and the combination of a neighbour mutation and a uniform mutation is applied to balance exploitation and exploration. A real vector is used to represent the corresponding crosssectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model was developed to deal with more practical considerations in modelling, such as the existence of members, kinematic stability analysis, and computation of stresses and displacements. Crosssectional areas and node connections are decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions, different topologies can be found using the proposed algorithm on a single run and some trusses have smaller weights than the solutions in the literature.

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A Study of the Application of Chaos to the Genetic AlgorithmJegede, Olawale 10 April 2014 (has links)
This work focuses on the use of a genetic algorithm for optimization in a searchbased problem. The Genetic Algorithm (GA) is a subset of evolutionary algorithms that models biological processes to optimize highly complex functions. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” (i.e. minimize the objective function). A major advantage of using GA over most stochastic techniques is its parallelism, which speeds up the simulation results leading to faster convergence. With mutation, the GA is also less likely to get stuck in local minima compared to other stochastic techniques.
However, some notable drawbacks of the Standard GA (SGA) include slow convergence and a possibility of being stuck in local optimum solution. The SGA uses a random process to generate parameter values for the initial population generation, crossover and mutation processes. Random number generators are designed to result in either uniform distributions or Gaussian distributions. We conjecture that the evolutionary processes in genetics are driven by a random nonlinear deterministic dynamic process rather than a random nondeterministic process. Therefore, in the GA evolutionary process, a chaotic map is incorporated into the initial population generation, the crossover and mutation processes of the SGA; this is termed the Chaotic GA (CGA).
The properties of a chaotic system that provides additional benefits over randomly generated solutions are sensitivity to initial conditions, topological density and topological transitivity (robust diversity). These properties ensure that the CGA is able to explore the entire solution space. Introducing chaos into the whole process of a standard genetic algorithm may help improve convergence time and accuracy. Simulation was done using Matlab and Java.

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A Domain Aware Genetic Algorithm for the pMedian ProblemVickers, Dennis 01 January 2011 (has links)
The pmedian problem is an NPcomplete combinatorial optimization problem often used in the fields of facility location and clustering. Given a graph with n nodes and an integer p < n, the pmedian problem seeks a set of p medians such that the sum of the distances of the n nodes from their nearest median is minimized. This dissertation develops a genetic algorithm that generates solutions to the pmedian problem that improves on previously published genetic algorithms by implementing operators that exploit domain specific information. More specifically, this GA explores the following:
(1) The advantages of using "good" solutions generated using extant heuristics in the initial generation of chromosomes.
(2) The effectiveness of a crossover operation that exchanges centers in the same neighborhood rather than exchanging arbitrarily chosen subsets of centers.
(3) The efficacy of using a biased mutation operator that favors replacing existing medians from less fit chromosomes with nonmedian nodes from the same neighborhood as the median being replaced.
Using published problem sets with known solutions, this dissertation examines solutions identified by the new genetic algorithm in order to determine the accuracy, efficiency and performance characteristics of the new algorithm. In addition, it tests the contribution of each of the algorithm's operators by systematically controlling for all the other factors.
The results of the analysis showed that integrating operators that exploited domain specific information did have an overall positive impact on the genetic algorithm. In addition, the results showed that using a structured initial population had little impact on the algorithm's ability to find an optimal solution but it did create a better initial solution and allowed the algorithm to produce a relatively good solution early in the search. Also, the analysis showed that a directed approach to crossover operations was effective and produced superior solutions. Finally, the analysis showed that a directed approach toward mutation did not have a large impact on the overall functionality of the algorithm and may be inferior to an arbitrary approach to mutation.

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Evolutionary optimisation for electromagnetics designKemp, Benjamin January 2000 (has links)
No description available.

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Multidisciplinary optimisation using evolutionary computingKhatib, Wael January 1999 (has links)
No description available.

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Automated comparative protein modellingMay, Alexander Conrad William January 1996 (has links)
No description available.

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Multiple sequence alignment pomocí genetických algoritmů / Multiple sequence alignment using genetic algorithmsPátek, Zdeněk January 2012 (has links)
Title: Multiple sequence alignment using genetic algorithms Author: Zdeněk Pátek Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: The thesis adresses the problem of multiple sequence alignment (MSA). It contains the specication of the proposed method MSAMS that allows to find motifs in biological sequences, to split sequences to blocks using the motifs, to solve MSA on the blocks and nally to assemble the global alignment from the aligned blocks and motifs. Motif search and MSA are both solved using genetic algorithms. The thesis describes the implementation of the method, conguration of its settings, benchmarking on the BAliBASE database and comparison to the ClustalW program. Experimental results showed that MSAMS can discover better alignments than ClustalW. Keywords: multiple sequence alignment, motif nding, genetic algorithms, ClustalW

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Genetic detection with application of time series analysis呂素慧 Unknown Date (has links)
This article investigates the detection and identification problems for changing of regimes about nonlinear time series process. We apply the concept of genetic algorithm and AIC criterion to test the changing of regimes. This way is different from traditional detection methods According to our statistical decision procedure, the mean of moving average and the genetic detection for the underlying time series will be considered to decide change points. Finally, an empirical application about the detection and identification of change points for the Taiwan Business Cycle is illustrated.

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