In this paper a novel technique for automatic data clustering based on the ARTIFICIAL IMMUNE ALGORITHM is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the IMMUNE ALGORITHM to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed ALGORITHM (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering system. The method has been tested on the different kinds of the complex ARTIFICIAL data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering ALGORITHM (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic ALGORITHM based clustering technique (as an evolutionary clustering ALGORITHM).

Uplift Modeling aims to detect subgroups in a population with a specific response or reaction to an action taken on the targeted group. In these models, the Treatment set contains objects that have been exposed to some action, such as a marketing campaign or clinical treatment, while in the Control set, they have not. In this study, a novel ARTIFICIAL IMMUNE system-based model was designed using an AIRS classifier to solve uplift modeling problems with improved efficiency. In this approach, a predictive model was built for estimating the conditional probability of receiving the desired response from the subpopulation that has taken the action over the relevant probability of the sub-population that has not taken the action. The proposed model was tested on the Hillstorm-visit-w dataset. Experimental results showed a 138 percent improvement in the area under the uplift curve which is a measure to assess an uplift model's performance.

This article introduces two swarm intelligent ALGORITHMs, a group search optimizer (GSO) and an ARTIFICIAL fish swarm ALGORITHM (AFSA). A single intelligent ALGORITHM always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these ALGORITHMs to create a new hybrid optimization ALGORITHM known as the group search-ARTIFICIAL fish swarm ALGORITHM (GS-AFSA). This ALGORITHM has been applied to three different discrete truss optimization problems. The optimization results are compared with those obtained using the standard GSO, the AFSA and the quick group search optimizer (QGSO). The proposed GS-AFSA eliminated the shortcomings of GSO regarding falling into the local optimum by taking advantage of AFSA’s stable convergence characteristics and achieving a better convergence rate and convergence accuracy than the GSO and the AFSA. Furthermore, the GS-AFSA has a superior convergence accuracy compared to the QGSO, all while solving a complicated structural optimization problem containing numerous design variables.