There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation.

Intrusion detection systems (IDS) are important to protect our systems and networks from attacks and malicious behaviors. In this paper, we propose a new hybrid intrusion detection system by using accelerated genetic algorithm and rough set theory (AGAAR) for data feature reduction, and genetic programming with local search (GPLS) for data classification. The AGAAR method is used to select the most relevant attributes that can represent an intrusion detection dataset. In order to improve the performance of GPLS classifier, a new local search strategy is used with genetic programming operators. The main target of using local search strategy is to discover the better solution from the current. The results shown later indicate that classification accuracy improved from 75.98% to 81.44% after using AGAAR attribute reduction for the NSL-KDD dataset. The classification accuracies have been compared with others algorithms and shown that the proposed method can be one of the competitive classifiers for IDS.

Attribute reduction is the process of removing a subset of attributes from the dataset. One of the most famous tools used for solving the attribute reduction problem is rough set theory. The current attribute reduction methods in rough set theory are failed for finding the optimal reduction because of no perfect heuristic can ensure optimality. In this paper, we consider a novel rough set approach to attribute reduction based on heuristic genetic algorithm. The proposed method, called accelerated genetic algorithm attribute reduction (AGAAR). The proposed method uses new suitable crossover and mutation operators that fit the considered problem. Moreover, an acceleration technique is also invoked in order to accelerate the search process for the optimal reduction. The experiment is archived to AGAAR through 13 well-known datasets from UCI machine learning repository. The experiment proves that the algorithm is more effective, it has improved the global search ability to avoid falling into local optimum, and it can get relative minimum attribute reduction.

In this paper, we present a modified on-demand routing algorithm for mobile ad-hoc networks (MANETs). The proposed algorithm is based on both the standard Ad-hoc On-demand Distance Vector (AODV) protocol and ant colony based optimization. The modified routing protocol is highly adaptive, efficient and scalable. The main goal in the design of the protocol was to reduce the routing overhead, response time, end-to-end delay and increase the performance. We refer to the new modified protocol as the Multi-Route AODV Ant routing algorithm (MRAA).

The search for the global minimum of a potential energy function is very difficult since the number of local minima grows exponentially with the molecule size. The present work proposes the application of genetic algorithm and tabu search methods, which called GAMCP (Genetic Algorithm with Matrix Coding Partitioning), and TSVP (Tabu Search with Variable Partitioning), respectively, for minimizing the molecular potential energy function. Computational results for problems with up to 200 degrees of freedom are presented and favorable compared with other three existing methods from the literature. Numerical results show that the proposed two methods are promising and produce high quality solutions with low computational costs.

Evolutionary programming (EP) is one of the main classes of evolutionary algorithms (EAs). Improving existing EAs is necessary in order to achieve better results and overcome their costly computational complexity. In this paper, we present a new version of EP called Directed Evolutionary Programming (DEP) in which more directing strategies with learned termination criteria are invoked to overcome some drawbacks of EP. In DEP, the mutated children are given the chance to improve themselves with the guidance of their parents. The search process in DEP is supported by diversification and intensification schemes in order to keep the diversity, achieve faster convergence and equip the search with an automatic termination criteria. The computational experiments show that DEP is efficient and cheaper than some well-known versions of EP.