Diversity-Driven Selection Operator for Combinatorial Optimization

A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Numerical experiments were performed considering four different algorithms in 24 instances of seven combinatorial optimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives

Integer programming techniques for educational timetabling

Educational timetabling problems require the assignment of times and resources to events, while sets of required and desirable constraints must be considered. The XHSTT format was adopted in this work because it models the main features of educational timetabling and it is the most used format in recent studies in the field. This work presents new cuts and reformulations for the existing integer programming model for XHSTT. The proposed cuts improved hugely the linear relaxation of the formulation, leading to an average gap reduction of 32%. Applied to XHSTT-2014 instance set, the alternative formulation pro- vided four new best known lower bounds and, used in a matheuristic framework, improved eleven best known solutions. The computational experiments also show that the resulting integer programming mod- els from the proposed formulation are more effectively solved for most of the instances

Siderophore production by Bacillus megaterium : effect of growth-phase and cultural conditions

Siderophore production by Bacillus megaterium was detected, in an iron-deficient culture medium, during the exponential growth phase, prior to the sporulation, in the presence of glucose; these results suggested that the onset of siderophore production did not require glucose depletion and was not related with the sporulation. The siderophore production by B. megaterium was affected by the carbon source used. The growth on glycerol promoted the very high siderophore production (1,182 μmol g−1 dry weight biomass); the opposite effect was observed in the presence of mannose (251 μmol g−1 dry weight biomass). The growth in the presence of fructose, galactose, glucose, lactose, maltose or sucrose, originated similar concentrations of siderophore (546–842 μmol g−1 dry weight biomass). Aeration had a positive effect on the production of siderophore. Incubation of B. megaterium under static conditions delayed and reduced the growth and the production of siderophore, compared with the incubation in stirred conditions.The authors thank Porto University/Totta Bank for their financial support through the project "Microbiological production of chelating agents" (Ref: 180). The authors also thank the Fundacao para a Ciencia e a Tecnologia (FCT) through the Portuguese Government for their financial support of this work through the grants Strategic project-LA23/2013-2014 (IBB) and PEST-C/EQB/LA0006/2011 (REQUIMTE). Manuela D. Machado gratefully acknowledges the postdoctoral (SFRH/BPD/72816/2010) grant from FCT

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Permutation-based optimization for the load restoration problem with improved time estimation of maneuvers

After the occurrence of faults in a radial distribution system, the load restoration problem consists in implementing a sequence of switch opening and closing operations such that the resulting network configuration restores services to the most loads in the shortest possible time. We formulate this optimization problem in terms of two complementary objectives, minimizing simultaneously the energy not supplied and the power not restored. The search space is encoded as a set of permutation vectors containing all maneuverable switches, and the decoding mechanism always guarantees feasibility and allows for multiple solutions per vector. In order to cope with the possibly large search space, an efficient reduction mechanism is proposed to decrease the number of allowed permutations. The resulting optimization problem is solved using Simulated Annealing followed by a local search refinement. The time taken to perform the maneuvers is estimated using a scheduling approach, which takes into account the existence of multiple dispatch teams and thus provides a more reliable computation than the usual approach of using the number of switch operations. The proposed method is validated using known optimal results in small problem instances, and is able to return significantly better results when compared against a Branch and Bound method with a pruning heuristic in a more complex scenario

Scheduling maneuvers for the restoration of electric power distribution networks: Formulation and heuristics

When a fault occurs in a power distribution network, energy utilities have limited time to define and run a restoration plan. While this problem is widely studied in the literature, existing works consider neither the work in parallel of multiple maintenance teams, nor the time taken by the teams to move between locations. In this paper, we address the problem of assigning and sequencing maneuver operations of a restoration plan. We propose modeling this situation as a scheduling problem, and then present specific heuristics for its solution. Computational experiments show that ignoring multiple teams and time requirements leads to solutions that seem to be efficient, but are in fact not. The proposed heuristics are able to quickly return good solutions, even for large instances, allowing their use within the constrained time frames required for the resolution of power outages

Hybrid deep learning approach for financial time series classification

This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the combined RBM + SVM techniques showed better performance when compared to the isolated SVM, which suggests that the proposed approach can be suitable for the considered application

Bi-objective Combined Facility Location and Network Design

Abstract. This paper presents a multicriterion algorithm for dealing with joint facility location and network design problems, formulated as bi-objective problems. The algorithm is composed of two modules: a multiobjective quasi-Newton algorithm, that is used to find the location of the facilities; and a multiobjective genetic algorithm, which is responsible for finding the efficient topologies. These modules are executed in an iterative way, to make the estimation of whole Pareto set possible. The algorithm has been applied to the expansion of a real energy distribution system. The minimization of financial cost and the maximization of reliability have been considered as the design objectives in this case.

Feedback-control operators for improved Pareto-set description: application to a polymer extrusion process

This paper presents a new class of operators for multiobjective evolutionary algorithms that are inspired on feedback-control techniques. The proposed operators, the archive-set reduction and the surface-filling crossover, have the purpose of enhancing the quality of the description of the Pareto-set in multiobjective optimization problems. They act on the Pareto-estimate sample set, performing operations that eliminate archive points in the most crowded regions, and generate new points in the less populated regions, leading to a dynamic equilibrium that tends to generate a uniform sampling of the efficient solution set. The internal parameters of those operators are coordinated by feedback-control inspired techniques, which ensure that the desired equilibrium is attained. Numerical experiments in some benchmark problems and in a real problem of optimization of a single screw extrusion system for polymer processing show that the proposed methodology is able to generate more detailed descriptions of Pareto-optimal fronts than the ones produced by usual algorithms.This work was supported by the Brazilian agencies CNPq, CAPES and FAPEMIG. The authors also acknowledge the support by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme