One of the main drawbacks of evolutionary algorithms is their great amount of parameters. Every step to lower this quantity is a step in the right direction. Automatic control of variation operators application rates ...
详细信息
ISBN:
(纸本)9783642214981
One of the main drawbacks of evolutionary algorithms is their great amount of parameters. Every step to lower this quantity is a step in the right direction. Automatic control of variation operators application rates during the run of an evolutionary algorithm is a desirable feature for two reasons: we are lowering the number of parameters of the algorithm and making it able to react changes in the conditions of the problem. In this paper, a dynamic breeder able to adapt the operators application rates over time following the evolutionary process is proposed. The decision to raise or to lower every rate is based on ANOVA to be sure of statistical significant.
During the space electronic system in carries out the exploratory mission in the deep space, it maybe faced with kinds of violent natural environment, to electric circuit's performance, the volume, the weight and ...
详细信息
ISBN:
(纸本)9783037850411
During the space electronic system in carries out the exploratory mission in the deep space, it maybe faced with kinds of violent natural environment, to electric circuit's performance, the volume, the weight and the stability proposed a higher request, the traditional circuit design method already more and more with difficulty satisfied this kind of request. The traditional circuit design method already more and more with difficulty satisfied this kind of request. But unifies the programmable component and the evolutionary algorithms hardware may the dynamic change hardware's structure adapt the adverse circumstance, resume the damage of the function, the adaptation for the duty change. After the optimization, obtains the circuit structure will often stem from our anticipation, this will be the altitude which the experience and the skillful institute hope to attain with difficulty. In view of the Xilinx Company's FPGA unique feature, proposed one kind of evolutionary algorithms which uses in the space electronic system circuit optimization design and through the experiment proved, the algorithm obtains the circuit structure to surpass the tradition circuit design method. This work investigates the application of genetic algorithms in the field of circuit optimization. For the case studies, this means has proved to be efficient and the experiment results show that the new means have got the better results.
This paper provides a systematic comparison of four evolutionary optimization algorithms;elitism based genetic algorithm, particle swarm optimization, ant colony optimization and artificial bee colony optimization in ...
详细信息
ISBN:
(纸本)9783642257339
This paper provides a systematic comparison of four evolutionary optimization algorithms;elitism based genetic algorithm, particle swarm optimization, ant colony optimization and artificial bee colony optimization in terms of their performance with respect to population size, convergence, fitness evaluation and percentage error on an interdisciplinary problem. The case in point is optimized design of high performance concrete mix. The methodology consists of two stages. In the first stage, a huge data base of 450 mix designs garnered through standard research publications were statistically analyzed to elicit upper and lower bounds of certain range constraints and rational ratio constraints of functional parameters. In the second stage, the four algorithms were applied to find the optimized quantities of ingredients constituting the mix. The results indicated that GA was bit high on errors, the other three algorithms showed almost same percentage of error. The convergence of bee colony optimization algorithm was fast followed by particle swarm optimization.
Neural network and evolutionary computation are important components of computational intelligence. This paper researches the architecture optimization of BP neural network of adaptive genetic algorithm (AGA). Combini...
详细信息
Neural network and evolutionary computation are important components of computational intelligence. This paper researches the architecture optimization of BP neural network of adaptive genetic algorithm (AGA). Combining AGA and BP neural network based on the characteristic of larger search scope of BP neural network optimization to make the crossover probability and mutation probability dynamically adjust with the evolution and approximate the function with the optimized BP network. The combination of AGA and BP network can achieve a complementary result, exert their respective strengths and improve the network performance. The simulation result shows that the method of this paper can enhance the generalization ability of the network. The network is more compact without redundant nodes and has better network fitting result and smaller error and the convergence speed of the network has been improved.
This paper introduces a methodology to reconstruct underwater environment using two images of the same scene, acquired by an acoustical camera from different points of view. The final target of the work is to produce ...
详细信息
ISBN:
(纸本)9781457700866
This paper introduces a methodology to reconstruct underwater environment using two images of the same scene, acquired by an acoustical camera from different points of view. The final target of the work is to produce a full 3D representation of the observed environment to improve its exploration and analysis. Indeed, as the DIDSON acoustic camera provides sequences of 2D images (distance and azimuth), the challenge consists in determining the missing elevation information about the observed scene in order to reconstruct (x,y,z) models, through the computation of the geometrical transformation between the acquisition view points, using image information only. Our research work is divided in two important steps. The first step which is feature point extraction allows robust and shape representative point extraction [1]. The second step presented in this paper uses these specific points appearing on two images and paired accordingly, to determine camera motion (rotation and translation) between the two acquisitions, and points missing elevation in order to reconstruct the observed scene. Due to the problem high-dimensional search space (6 camera motion parameters plus one elevation per pair of points), we propose to achieve the search using CMA-ES optimization algorithm. This stereovision-like optimization procedure assumes a known camera model. The first topic in this paper tries to check the good behavior of the supposed camera model in order to be sure that extracted points from images are robust enough and not affected by extra camera distortions. A set of DIDSON images have been acquired in the Laval University pool and used to perform such a verification, with various objects (wooden boxes and grid) observed from different points of view. Finally, using extracted pairs of points coming from two images, the proposed algorithm is able to retrieve the local relative geometry of the observed scene through the estimation of the missing elevations.
作者：
Li, JinlongLu, GuanzhouYao, XinUniv Sci & Technol China
Sch Comp Sci & Technol Nat Inspired Computat & Applicat Lab NICAL Joint USTC Birmingham Res Inst Intelligent Comput Hefei 230026 Anhui Peoples R China Univ Birmingham
Sch Comp Sci CERCIA Birmingham B15 2TT West Midlands England
Unique Input Output (UIO) sequences are used in conformance testing of Finite state machines (FSMs). evolutionary algorithms (EAs) have recently been employed to search UIOs. However, the problem of tuning evolutionar...
详细信息
ISBN:
(纸本)9783642249570;9783642249587
Unique Input Output (UIO) sequences are used in conformance testing of Finite state machines (FSMs). evolutionary algorithms (EAs) have recently been employed to search UIOs. However, the problem of tuning evolutionary algorithm parameters remains unsolved. In this paper, a number of features of fitness landscapes were computed to characterize the UIO instance, and a set of EA parameter settings were labeled with either 'good' or 'bad' for each UIO instance, and then a predictor mapping features of a WO instance to 'good' EA parameter settings is trained. For a given UIO instance, we use this predictor to find good EA parameter settings, and the experimental results have shown that the correct rate of predicting 'good' EA parameters was greater than 93%. Although the experimental study in this paper was carried out on the UIO problem, the paper actually addresses a very important issue, i.e., a systematic and principled method of tuning parameters for search algorithms. is the first time that a systematic and principled framework has been proposed in Search-Based Software Engineering for parameter tuning, by using machine learning techniques to learn good parameter values.
In this paper, a new class of evolutionary Algorithm (EA) named as Genetic Folding (GF) is introduced. GF is based on novel chromosomes organisation which is structured in a parent form. In this paper, the model selec...
详细信息
ISBN:
(纸本)9780857291295
In this paper, a new class of evolutionary Algorithm (EA) named as Genetic Folding (GF) is introduced. GF is based on novel chromosomes organisation which is structured in a parent form. In this paper, the model selection problem of Support Vector Machine (SVM) kernel' has been utilised as a case study. Five UCI datasets have been tested and experimental results are compared with other methods. As a conclusion, the proposed algorithm is very promising and it can be applied to solve further complicated domains and problems.
Adaptive evolutionary algorithms have been widely developed to improve the management of the balance between intensification and diversification during the search. Nevertheless, this balance may need to be dynamically...
详细信息
ISBN:
(纸本)9783642203633
Adaptive evolutionary algorithms have been widely developed to improve the management of the balance between intensification and diversification during the search. Nevertheless, this balance may need to be dynamically adjusted over time. Based on previous works on adaptive operator selection, we investigate in this paper how an adaptive controller can be used to achieve more dynamic search scenarios and what is the real impact of possible combinations of control components. This study may be helpful for the development of more autonomous and efficient evolutionary algorithms.
Standard Cooperative Co-evolution uses a round-robin method to select subcomponents to undergo optimization. In a non-separable (epistatic) optimization problem, dividing the computational budget equally between all o...
详细信息
ISBN:
(纸本)9781450305570
Standard Cooperative Co-evolution uses a round-robin method to select subcomponents to undergo optimization. In a non-separable (epistatic) optimization problem, dividing the computational budget equally between all of the subcomponents is not necessarily the best strategy. When dealing with non-separable problems, there is usually an imbalance between the contribution of various subcomponents to the global fitness of the individuals. Using a round-robin fashion treats all of the subcomponents equally and wastes the computational budget. In this paper, we propose a Contribution Based Cooperative Co-evolution (CBCC) that selects the subcomponents based on their contributions to the global fitness. This alleviates the imbalance issue and allows the computational resources to be used more efficiently. Experiments on several benchmark functions with the "imbalance issue" show that this new scheme is promising, especially when it is combined with a grouping algorithm that captures interacting variables in common subcomponents.
This work presents a method for evolving finite state machines for the classification of polymerase chain reaction primers in mice using graph based evolutionary algorithms. Using these machine learning tools we can c...
详细信息
ISBN:
(纸本)9781424478354
This work presents a method for evolving finite state machines for the classification of polymerase chain reaction primers in mice using graph based evolutionary algorithms. Using these machine learning tools we can compensate for many lab, organism, and chemical specific factors that can cause these primers to fail. Using Finite State Classifiers can help to decrease the number of primers that fail to amplify correctly. For training these classifiers, fifteen different graph based evolutionary algorithms were used in two different experiments to explore the effects of diversity preservation on the development of these classifiers. By controlling the rate at which information is shared in the evolving population, classifiers with a high likelihood of not accepting bad primers were found. This proposed tool can act as a post-production add-on to the standard primer picking algorithm for gene expression detection in mice to compensate for local factors that may induce errors.
暂无评论