Protein multiple sequence alignment by hybrid bio-inspired algorithms

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space

Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques

With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools

Narzisi G.; “Real Coded Clonal Selection Algorithm for Unconstrained Global Numerical Optimization using a Hybrid Inversely

Categories and Subject Descriptor

Automated deduction in Topology: two different approaches

Two approaches to theorem proving in Topology are described and some research problems in the field are given

A characterization of rational amalgamation operations.

This paper deals with amalgamation of fuzzy opinions when a fixed number of individuals is faced with an unknown number of alternatives. The aggregation rule is defined by means of intensity aggregation operations that verify certain ethical conditions, and assuming fuzzy rationality as defined in [1, 2]. A necessary and sufficient condition for non-irrationality is presented, along with comments on the importance of the number of alternatives

A Model for Amalgamation in Group Decision Making

this paper a welfare oriented approach (similar to Arrow's model) has been developed, but not a decision oriented one. Real democratic problems are more related to decision making problems, and in this case an analysis of the stability of the final decision should also be included (see for example [Mon90]). In any case, by using fuzzy preference relations, we have been able not only to avoid Arrow's paradox but also other similar restrictive results in the fuzzy context (see [FF75] and also [Mon85, Mon88a]

On the Convergence of Immune Algorithms

Abstract — Immune Algorithms have been used widely and successfully in many computational intelligence areas including optimization. Given the large number of variants of each operator of this class of algorithms, this paper presents a study of the convergence properties of Immune Algorithms in general, conducted by examining conditions which are sufficient to prove their convergence to the global optimum of an optimization problem. Furthermore problem independent upper bounds for the number of generations required to guarantee that the solution is found with a defined probability are derived in a similar manner as performed previously, in literature, for genetic algorithms. Again the independence of the function to be optimised leads to an upper bound which is not of practical interest, confirming the general idea that when deriving time bounds for Evolutionary Algorithms the problem class to be optimised needs to be considered. I

Equivalence and compositions of fuzzy rationality measures.

An axiomatic basis for fuzzy rationality measures has already been introduced by the authors in a previous paper [5], formalizing the fact that there exist degrees of consistency when preferences over a fixed set of alternatives are expressed in terms of fuzzy binary preference relations. This paper deals with some practical consequences. On the one hand, similarities and compositions of fuzzy rationality measures are considered, showing natural ways of deriving new measures; on the other, if basic stability properties are introduced in order to assure that small intensity measurement errors never lead to big changes in the associate rationality value, it is shown that crisp (i.e., binary) rationality measures present serious difficulties when applied to fuzzy preference relations