Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Information Retrieval-Based Optimization Approaches for Requirement Traceability Recovery

Requirements traceability provides support for important software engineering activities. Requirements traceability recovery (RTR) is becoming increasingly important due to the numerous benefits to the overall quality of software. Improving the RTR problem has become an active topic of research for software engineers; researchers have proposed a number of approaches for improving and automating RTR across the requirements and the source code of the system. Textual analysis and Information Retrieval (IR) techniques have been applied to the RTR problem for many years; however, most of the existing IR-based methodologies applied to the RTR problem are semiautomatic or time-consuming, even though many links are correctly recovered using IR. Thus, there is a need for effective and innovative approaches for automatization in the RTR problem. In this research, we study IR techniques applied to the RTR problem to determine the optimal alternative to RTR across the textual content of requirements and system source code, and propose innovative methodologies based on computational intelligence combine with IR to achieve automatization. We approach the study of the RTR problem as an optimization problem; the problem is formulated as a multi or mono objective search in which we assign one-to-many relationships between each requirement and source code classes by considering similarity in their textual content. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Artificial Bee Colony (ABC), when combined with IR techniques, appear to provide promising alternatives for finding a complete and accurate list of traceability links. We adapt the NGSA-II and ABC algorithms to solve the RTR problem, generate programing tools for experimentation, and report the results on three open source projects. Results show values of precision and recall above 70%. NSGA-II and ABC are also analyzed based on time complexity using the big-O notation; results indicate NSGA-II is more time efficient and less precise than ABC

Improving Artificial Bee Colony Algorithm with Evolutionary Operators

In this paper, we analyze the effect of replacing the method to create new solutions in artificial bee colony algorithm by recombination operators. Since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases

Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition

The base system in this paper uses Hidden Markov Models (HMMs) to model dynamic relationships among facial features in facial behavior interpretation and understanding field. The input of HMMs is a new set of derived features from geometrical distances obtained from detected and automatically tracked facial points. Numerical data representation which is in the form of multi-time series is transformed to a symbolic representation in order to reduce dimensionality, extract the most pertinent information and give a meaningful representation to humans. The main problem of the use of HMMs is that the training is generally trapped in local minima, so we used the Differential Evolution (DE) algorithm to offer more diversity and so limit as much as possible the occurrence of stagnation. For this reason, this paper proposes to enhance HMM learning abilities by the use of DE as an optimization tool, instead of the classical Baum and Welch algorithm. Obtained results are compared against the traditional learning approach and significant improvements have been obtained.</p

An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's $t$-test, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases