Because of the growing reliance that corporations and government agencies place on their computer networks, the significance of defending these systems from attack cannot be underestimated. Computer security and forensics are crucial in protecting systems from attack or from unauthorized access that might have undesirable consequences, and can help to ensure that a network infrastructure will r...

| This paper introduces a new tournament selection algorithm that can be used for evolutionary path planning systems. The fuzzy tournament selection algorithm (FTSA) described in this paper selects candidate paths (CPs) to be parents and undergo reproduction based on: (1) the euclidean distance of a path from the origin to its destination, (2) the sum of the changes in the slope of a path, and ...

Artificial immune systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. Their development and application domains follow those of soft computing paradigms such as artificial neural networks (ANN), evolutionary algorithms (EA) and fuzzy systems (FS). Despite some isolated efforts,...

In this contribution, the biological problem of extracting promoters (composed of two nucleotide sequences, TTGACA and TATAAT, separated by among 15 and 22 pairs of bases) from E. coli DNA sequences is tackled. Classical approaches for this problem, based on considering probabilistic models of the promoter motifs, fail at performing accurate predictions due to the difficulty of properly integra...

Purpose – This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference-based decision support system that uses an evolutionary algorithm (EA) to optimize the if-then rules and its parameters. The performance of the proposed method is compared with a fuzzy in...

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems ...

In this paper we will examine the problem of learning an e cient fuzzy logic rule set for the control of the inverted pendulum (with nonlinear dynamics) using an evolutionary algorithm. In particular we compare a two layered rule set with a single fuzzy logic rule set. Furthermore we look at the e ect that di erent choices of objective function (in the evolutionary algorithm) have on the rule s...

In this article, we propose a new approach to the virus DNA–based evolutionary algorithm (VDNA-EA) to implement self-learning of a class of Takagi-Sugeno (T-S) fuzzy controllers. The fuzzy controllers use T-S fuzzy rules with linear consequent, the generalized input fuzzy sets, Zadeh fuzzy logic and operators, and the generalized defuzzifier. The fuzzy controllers are proved to be nonlinear pro...

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. Three different multi-objective evolutionary algorithms (MONEA, ENORA and NSGA-II) are used together with neural network techniques. These algorithms are checked out in the approximation of a dynamic non...