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— Parallel processing has emerged as a key enabling technology in modern computing. Recent software advances have allowed collections of heterogeneous computers to be used as a concurrent computational resource. In this work we explore how Differential Evolution can be parallelized, using a ring–network topology, so as to improve both the speed and the(More)
(ISSN 1568-4946). This version has been peer-reviewed but does not include the final publisher proof corrections, published layout or pagination. All articles available through Birkbeck ePrints are protected by intellectual property law, including copyright law. Any use made of the contents should comply with the relevant law. (2004). Neural network-based(More)
The Particle Swarm Optimizer, like many other evolutionary and classical minimization methods, suuers the problem of occasional convergence to local minima, especially in multimodal and scattered landscapes. In this work we propose a modiication of the Particle Swarm Optimizer that makes use of a new technique, named Function \Stretching", to alleviate the(More)
—Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our(More)
In this paper a new clustering operator for Evolutionary Algorithms is proposed. The operator incorporates the unsupervised k–windows clustering algorithm , utilizing already computed pieces of information regarding the search space in an attempt to discover regions containing groups of individuals located close to different minimizers. Consequently, the(More)
— A parallel, multi–population Differential Evolution algorithm for multiobjective optimization is introduced. The algorithm is equipped with a domination selection operator to enhance its performance by favoring non–dominated individuals in the populations. Preliminary experimental results on widely used test problems are promising. Comparisons with the(More)
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid framework that combines the(More)
This study presents an approach to automatically detect tumors in colonoscopic images that is based on the synergy between unsupervised clustering and artificial neural networks. First the noisy data set is partitioned into clusters and then a different neural network is trained from data of each detected cluster. Each network is therefore considered a "(More)
Evolutionary Algorithms (EAs) are nature inspired problem solving optimization algorithms, which employ computational models of evolutionary processes. Various Evolutionary Algorithms have been proposed in the literature. The most important The algorithms mentioned above share the common conceptual base of simulating the evolution of a population of(More)
The development of microarray technologies gives scientists the ability to examine, discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for(More)