Multi-objective improvement of software using co-evolution and smart seeding

Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner

Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled

Part 1: Digital ServicesInternational audienceQuantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-controlled into QPSO (QPSO-DC) to enhance the diversity of particle swarm, and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DC has stronger global search ability than QPSO and standard PSO

Implementation of a Classification-Based Prediction Model for Plant mRNA Poly(A) Sites

The poly(A) site of a messenger RNA (mRNA) defines the end of a transcript during eukaryotic gene expression. Finding poly(A) sites in genome sequences can help to annotate the ends of genes and predict alternative polyadenylation. However, it is challenging to predict plant poly(A) sites using computational methods because of the weak signals that determine the poly(A) sites. Here we describe a classification based plant poly(A) site recognition model. First, several feature representation methods like factorial moments, M encoding, and weight of signal patterns are adopted to describe the makeup of nucleotide sequences of poly(A) signals. Then, a training model using different classification algorithms like Bayesian Network is built as a testing model to predict plant mRNA poly(A) sites. Comparing to previous plant poly(A) sites prediction software PASS that we developed, the recognition model introduced here has better performance, flexibility and expansibility

A Fast Solution to the Partition Problem by Using Tissue-Like P Systems

Tissue-like P systems with cell division is a computing model in the framework of membrane computing based on the intercellular communication and cooperation between neurons. In such a model, the structure of the devices is a network of elementary cells. Tissue-like P systems with cell division have the ability of increasing the number of cells during the computation. In this paper we exploit this ability and present a polynomial-time solution to the (NP-complete) Partition problem via a uniform family of such P systems.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58