Mohsen Davarynejad

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Nature may have been the original inspiration for evolutionary algorithms, but unlike artificially designed systems, nature has an abundance of resources and time. For man-made systems, computational complexity is a prohibitive factor in sufficiently large and complex problems of today. Much of this computational complexity is due to the fitness function(More)
Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still a prohibitive factor in certain real-world applications involving expensive (computationally speaking) fitness function evaluations. In this paper, we depart from the observation(More)
Spread spectrum audio watermarking (SSW) is one of the most powerful techniques for secure audio watermarking. SSW hides information by spreading the spectrum. The hidden information is called the ‘watermark’ and is added to a host signal, making the latter a watermarked signal. The spreading of the spectrum is carried out by using a pseudo-noise (PN)(More)
Attack trees are a well-known formalism for quantitative analysis of cyber attacks consisting of multiple steps and alternative paths. It is possible to derive properties of the overall attacks from properties of individual steps, such as cost for the attacker and probability of success. However, in existing formalisms, such properties are considered(More)
Gene association/interaction networks have complex structures that provide a better understanding of mechanisms at the molecular level that govern essential processes inside the cell. The interaction mechanisms are conventionally modeled by nonlinear dynamic systems of coupled differential equations (Ssystems) adhering to the power-law formalism. Our(More)
Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computationally very expensive. With the proposed approach, the expensive fitness evaluation step is replaced by an approximate model. An intelligent guided technique via an adaptive fuzzy(More)
Complex optimization problems that cannot be solved using exhaustive search require efficient search metaheuristics to find optimal solutions. In practice, metaheuristics suffer from various types of search bias, the understanding of which is of crucial importance, as it directly pertinent to the problem of making the best possible selection of solvers. Two(More)
The standard reinforcement learning algorithms have proven to be effective tools for letting an agent learn from its experiences generated by its interaction with an environment. Among others, reinforcement learning algorithms are of interest because they require no explicit model of the environment beforehand and learning happens through trial and error.(More)