Hussein A. Abbass

Learn More
The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Multi-objective Optimization Problems (MOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle(More)
INTRODUCTION................................................................................................................................................ 1 BASIC IDEAS AND RATIONALE ........................................................................................................................... 1 IMMUNE PRINCIPLES(More)
The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm,(More)
In [9], we presented a modified version of Ant-Miner (i.e. Ant-Miner2), where the core computation heuristic value was based on a simple density estimation heuristic. In this paper, we present a further study and introduce another ant-based algorithm, which uses a different pheromone updating strategy and state transition rule. By comparison with the work(More)
In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with an evolutionary artificial neural network approach based on adaptive genetic algorithm with strong macro-search capability and global optimization, which was used to optimize initial weights and thresholds of the network. Experimental(More)
In this paper, we present a comparison between two multi-objective formulations to the formation of neuro-ensembles. The first formulation splits the training set into two non-overlapping stratified subsets and form an objective to minimize the training error on each subset, while the second formulation adds random noise to the training set to form a second(More)
Evolutionary Artificial Neural Networks (EANN) have been a focus of research in the areas of Evolutionary Algorithms (EA) and Artificial Neural Networks (ANN) for the last decade. In this paper, we present an EANN approach based on pareto multi-objective optimization and differential evolution augmented with local search. We call the approach Memetic Pareto(More)
The marriage in honey–bees optimization (MBO) algorithm was recently proposed and showed good results for combinatorial optimization problems. Contrary to most of the swarm intelligence algorithms (such as Ant Colony Optimization), MBO uses self-organization to mix different heuristics. This paper presents a variation of the MBO algorithm where the colony(More)