Vassilis P. Plagianakos

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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)
—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)
In this paper, Parallel Evolutionary Algorithms for integer weight neural network training are presented. To this end, each processor is assigned a subpopulation of potential solutions. The subpopulations are independently evolved in parallel and occasional migration is employed to allow cooperation between them. The proposed algorithms are applied to train(More)
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to(More)
—Handling multimodal functions is a very important and challenging task in evolutionary computation community, since most of the real-world applications exhibit highly multi-modal landscapes. Motivated by the dynamics and the proximity characteristics of Differential Evolution's mutation strategies tending to distribute the individuals of the population to(More)
In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and non–stationarity, a common approach is to combine a method for the partitioning of the input space into a number of subspaces with a local approximation scheme for each sub-space.(More)
— This papers proposes a novel self–adaptive scheme for the evolution of crucial control parameters in Evolutionary Algorithms. More specifically, we suggest to utilize the Differential Evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is(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)