18896200
iECGA: integer extended compact genetic algorithm
Extended compact genetic algorithm (ECGA) is an algorithm that can solve hard problems in the binary domain. ECGA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. In this paper, we propose a new algorithm that extends ECGA, called integer extended compact genetic algorithm (iECGA). iECGA uses a modified probability model and inherits the capability of detecting building blocks from ECGA. iECGA is specifically designed for problems in the integer domain and can avoid the difficulties that ECGA encounters. With the experimental results, we show the performance comparisons between ECGA, iECGA, and a simple GA. The results indicate that iECGA has good performance on problems in the integer domain.
14259579
FTXI: fault tolerance XCS in integer
In the realm of data mining, several key issues exists in the traditional classification algorithms, such as low readability, large rule number, and low accuracy with information losing. In this paper, we propose a new classification methodology, called fault tolerance XCS in integer (FTXI), by extending XCS to handle conditions in integers and integrating the mechanism of fault tolerance in the context of data mining into the framework of XCS. We also design and generate appropriate artificial data sets for examining and verifying the proposed method. Our experiments indicate that FTXI can provide the least rule number, obtain high prediction accuracy, and offer rule readability, compared to C4.5 and XCS in integer without fault tolerance.
34404148
Stochastic sketching: a new method for global optimization
Abstract In this paper, we propose Stochastic sketching for global optimization based on a simulation of human behaviour. Stochastic sketching tries to do things simply in the human way without too much interpretation instead of modeling the thought and strategies of human beings and applying an artificial model to problems. We introduce and discuss concepts and components essential to stochastic sketching in detail, including sampling guide, zooming controller, sketching model, precision threshold, and satisfaction probability. Experimental results of stochastic sketching on several test functions and a set of recommended parameter settings are given, as well as preliminary comparisons between stochastic sketching and related evolutionary algorithms including evolution strategies, evolutionary programming, and genetic algorithms.
34645664
Applying Family Competition to Evolution Strategies for Constrained Optimization
This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.
11560820
Introducing recombination with dynamic linkage discovery to particle swarm optimization
In this paper, we introduce the recombination operator with the technique of dynamic linkage discovery to particle swarm optimization (PSO) in order to improve the performance of PSO. Numerical experiments are conducted on a set of carefully designed benchmark functions and demonstrate good performance achieved by the proposed methodology.
2888098
Evolutionary interactive music composition
This paper proposes the CFE framework---Composition, Feedback, and Evolution---and presents an interactive music composition system. The system composes short, manageable pieces of music by interacting with users. The most important features of the system include creating customized music according to the user preference and providing the facilities specifically designed for producing large amounts of music. We present the structure as well as the implementation of the system and the auxiliary functionalities that enhance the system. We also introduce the auto-feedback test with which we verify and evaluate the interactive music composition system.
17662809
Hierarchical Organisation of Predicate-Semantic Models
This paper introduces techniques to organise predicate- semantic models in a hierarchical structure so that a new model can inherit the laws of the existing ones. Generic composition is used to simplify the manipulation of predicates. Necessary restrictions are imposed on the definition of each model so that the inheritance relation can be established by checking a few conditions on the healthiness conditions and the commands. Much of the checking can be supported by laws of generic composition. The techniques also help simplify the proof of well-definedness of program combinators (i.e. their closure in the semantic space) so that we no longer need to prove it (in predicate calculus) for every new model.
17733083
SML Model-based Management
Models enable a clear separation between domain knowledge and application-specific details. In the system management arena, there are multiple implementations of model-based system management solutions, but until now, there was no industry-wide agreement on a common language or paradigm to enable interoperability. A new standard has been proposed to describe IT Services, the service modeling language ("SML"). While SML enables interoperability, it still poses challenges in terms of scalable model stores, model validation, and use. This paper discusses an architecture for SML model management and validation and describe and evaluate a prototype implementation with a use case on system management of PlanetLab.
9845542
Back Propagation with Randomized Cost Function for Training Neural Networks
A novel method to improve both the generalization and convergence performance of the back propagation algorithm (BP) by using multiple cost functions with a randomizing scheme is proposed in this paper. Under certain conditions, the randomized technique will converge to the global minimum with probability one. Experimental results on benchmark Encoder-Decoder problems and the NC2 classification problem show that the method is effective in enhancing BP's convergence and generalization performance
17553402
Efficient Parallel Algorithms for Euclidean Distance Transform
The Euclidean Distance Transform (EDT) converts a binary image into one where each pixel has a value equal to its distance to the nearest foreground pixel. Two parallel algorithms for EDT transform on linear array with reconfigurable pipeline bus system (LARPBS) are presented. For an image with n×n pixels, the first algorithm can complete EDT transform in ) log log log log log log ( n n n O time using n processors. The second algorithm can compute the EDT in O( log n loglog n) time using n n log log 2 processors.
37750685
A Genetic Algorithm with Chromosome-Repairing Technique for Polygonal Approximation of Digital Curves
A genetic algorithm with chromosome-repairing scheme (CRS) is proposed in this paper to solve the polygonal approximation problem. Different from the existing approaches based on genetic algorithms, the proposed algorithm adopts variable-length chromosome encoding for reducing the memory storage and computational time, and develops a special crossover named gene-removing crossover for removing the redundant genes. It is known that Genetic operators may yield infeasible solutions, and it is generally difficult to cope with them. Instead of using the penalty function approach, we propose a chromosome-repairing scheme to iteratively add the valuable candidate gene to the chromosome to deal with the infeasible solution and an evaluating scheme for the candidate genes. The experimental results show that the proposed CRS outperforms the existing approaches based on genetic-algorithms, ant-colony-optimization and tabu-search.