Vassilis P. Plagianakos

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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 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 eachparticle in the swarm,wedevelop ahybrid framework that combines theParticle(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)
The Particle Swarm Optimizer, like many other evolutionary and classical minimization methods, su ers the problem of occasional convergence to local minima, especially in multimodal and scattered landscapes. In this work we propose a modi cation of the Particle Swarm Optimizer that makes use of a new technique, named Function \Stretching", to alleviate the(More)
In this paper, on-line training of neural networks is investigated in the context of computerassisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line Backpropagation is proposed and used to seed an on-line evolution process that applies a Differential Evolution Strategy to (re-)adapt the neural network to modified(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)
Networks of spiking neurons can perform complex non–linear computations in fast temporal coding just as well as rate coded networks. These networks differ from previous models in that spiking neurons communicate information by the timing, rather than the rate, of spikes. To apply spiking neural networks on particular tasks, a learning process is required.(More)
The hybridization and composition of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper we propose a hybrid approach that combines differential evolution mutation operators in an attempt to balance their exploration and exploitation capabilities. Additionally, a(More)
In this paper, Parallel Evolutionary Algorithms for integer weightneural network training are presented. To this end, each processoris assigned a subpopulation of potential solutions. Thesubpopulations are independently evolved in parallel andoccasional migration is employed to allow cooperation betweenthem. The proposed algorithms are applied to train(More)