International Journal on Artificial Intelligence Tools

This article presents COURSR2, an intelligent system aiming at managing the personal time of lifelong learners in an integrated way. Lifelong learners usually try to combine learning with their professional and family duties. Taking in advance informative decisions about whether they have the necessary time resources to devote to a learning object is crucial, both for psychological and for financial reasons. COURSR2 encompasses information for all temporal aspects of a learning object. For example, a course may have lectures, with cardinality constraints over attending them, labs, mid-term exams, final exams, homework, projects and home reading. The user can also represent his non-educational activities, using the general purpose ontology of the SELFPLANNER intelligent calendar application. By subscribing to a learning object through COURSR2, the scheduling engine of SELFPLANNER’s is employed to find viable plans, allowing rescheduling of existing activities. Thus, the learner is able to perform what-if analysis about whether a set of learning objects is compatible with each other, as well as with his other commitments. The latest version of COURSR2 has been deployed and evaluated using real users.

The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding. Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds. The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters. In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algorithm. To prove the efficacy of proposed algorithm, it is applied to solve multilevel thresholding problem in which the Kapur's entropy criterion is considered as figure-of-merit. In this experiment, four benchmark test images are considered for multilevel thresholding. The optimal values of thresholds are obtained using TLBO, MC and particle swarm optimization (PSO) in addition to proposed algorithm to accomplish the comparative study. To support the superiority of proposed algorithm over others, various quantitative and qualitative results are presented in addition to statistical analysis.

k-Means is among the most significant clustering algorithms for vectors chosen from an underlying space S. Its applications span a broad range of fields including machine learning, image and signal processing, and Web mining. Since the introduction of k-Means, two of its major design parameters remain open to research. The first is the number of clusters to be formed and the second is the initial vectors. The latter is also inherently related to selecting a density measure for S. This article presents a two-step framework for estimating both parameters. First, the underlying vector space is represented as a fuzzy graph. Afterwards, two algorithms for partitioning a fuzzy graph to non-overlapping communities, namely Fuzzy Walktrap and Fuzzy Newman-Girvan, are executed. The former is a low complexity evolving heuristic, whereas the latter is deterministic and combines a graph communication metric with an exhaustive search principle. Once communities are discovered, their number is taken as an estimate of the true number of clusters. The initial centroids or seeds are subsequently selected based on the density of S. The proposed framework is modular, allowing thus more initialization schemes to be derived. The secondary contributions of this article are HI, a similarity metric for vectors with numerical and categorical entries and the assessment of its stochastic behavior, and TD, a metric for assessing cluster confusion. The aforementioned framework has been implemented mainly in C# and partially in C++ and its performance in terms of efficiency, accuracy, and cluster confusion was experimentally assessed. Post-processing results conducted with MATLAB indicate that the evolving community discovery algorithm approaches the performance of its deterministic counterpart with considerably less complexity.

With the development of intelligent computation technology, the intelligent evolution algorithms have been widely applied to solve optimization problem in the real world. As a novel evolution algorithm, fruit fly optimization algorithm (FOA) has the advantages of simple operation and high efficiency. However, FOA also has some disadvantages, such as trapping into local optimal solution easily, failing to traverse the problem domain and limiting the universality. In order to cope with the disadvantages of FOA while retain it merits, this paper proposes AFOA, an adaptive fruit fly optimization algorithm. AFOA adjusts the swarm range parameter V dynamically and adaptively according to the historical memory of each iteration of the swarm, and adopts the more accurate elitist strategy, which is therefore very effective in both accelerating the convergence of the swarm to the global optimal front and maintaining diversity of the solutions. The convergence of the algorithm is firstly analyzed theoretically, and then 14 benchmark functions with different characteristics are executed to compare the performance among AFOA, PSO, FOA, and LGMS-FOA. The experimental results have shown that, AFOA algorithm is a new algorithm with global optimizing capability and high universality.

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

This paper presents a review of research reported on simulated annealing (SA). Different cooling/annealing schedules are summarized. Variants of SA are delineated. Recent applications of SA in engineering are reviewed.