A key challenge in decision fusion systems is to determine the best performing combination of local decision makers. This selection process can be performed statically at the training phase or dynamically at the execution ...

In this paper, we propose a limbic-based artificial emotional neural network (LiAENN) for a pattern

recognition problem. LiAENN is a novel computational neural model of the emotional brain that models

emotional ...

In this study, a hybrid of Quantum Evolutionary and Artificial Immune Algorithms (QIA) is proposed for solving Multiobjective Flexible Job Shop Scheduling Problem (MFJSSP). This problem is formulated as three-objective ...

In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian ...

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this

paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific

variant ...

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory ...