A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Application of computational intelligence to engineering

Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. This article shows how CI techniques overpass the strict limits of Artificial Intelligence field and can help solving real problems from distinct engineering areas: Mechanical, Computer Science and Electrical Engineering.

Application of genetic algorithms in the design of an electrical potential of fractional order

Fractional calculus (FC) is currently being applied in many areas of science and technology. In fact, this mathematical concept helps the researches to have a deeper insight about several phenomena that integer order models overlook. Genetic algorithms (GA) are an important tool to solve optimization problems that occur in engineering. This methodology applies the concepts that describe biological evolution to obtain optimal solution in many different applications. In this line of thought, in this work we use the FC and the GA concepts to implement the electrical fractional order potential. The performance of the GA scheme, and the convergence of the resulting approximation, are analyzed. The results are analyzed for different number of charges and several fractional orders.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

PhD thesis in Bioengineering

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Evolutionary switch and genetic convergence on rbcL following the evolution of C4 photosynthesis.

Rubisco is responsible for the fixation of CO2 into organic compounds through photosynthesis and thus has a great agronomic importance. It is well established that this enzyme suffers from a slow catalysis, and its low specificity results into photorespiration, which is considered as an energy waste for the plant. However, natural variations exist, and some Rubisco lineages, such as in C4 plants, exhibit higher catalytic efficiencies coupled to lower specificities. These C4 kinetics could have evolved as an adaptation to the higher CO2 concentration present in C4 photosynthetic cells. In this study, using phylogenetic analyses on a large data set of C3 and C4 monocots, we showed that the rbcL gene, which encodes the large subunit of Rubisco, evolved under positive selection in independent C4 lineages. This confirms that selective pressures on Rubisco have been switched in C4 plants by the high CO2 environment prevailing in their photosynthetic cells. Eight rbcL codons evolving under positive selection in C4 clades were involved in parallel changes among the 23 independent monocot C4 lineages included in this study. These amino acids are potentially responsible for the C4 kinetics, and their identification opens new roads for human-directed Rubisco engineering. The introgression of C4-like high-efficiency Rubisco would strongly enhance C3 crop yields in the future CO2-enriched atmosphere.

Histological and histochemical evaluation of human oral mucosa constructs developed by tissue engineering

Reconstruction of large oral mucosa defects is often challenging, since the shortage of healthy oral mucosa to replace the excised tissues is very common. In this context, tissue engineering techniques may provide a source of autologous tissues available for transplant in these patients. In this work, we developed a new model of artificial oral mucosa generated by tissue engineering using a fibrin-agarose scaffold. For that purpose, we generated primary cultures of human oral mucosa fibroblasts and keratinocytes from small biopsies of normal oral mucosa using enzymatic treatments. Then we determined the viability of the cultured cells by electron probe quantitative X-ray microanalysis, and we demonstrated that most of the cells in the primary cultures were alive and had high K/Na ratios. Once cell viability was determined, we used the cultured fibroblasts and keratinocytes to develop an artificial oral mucosa construct by using a fibrin-agarose extracellular matrix and a sequential culture technique using porous culture inserts. Histological analysis of the artificial tissues showed high similarities with normal oral mucosa controls. The epithelium of the oral substitutes had several layers, with desmosomes and apical microvilli and microplicae. Both the controls and the oral mucosa substitutes showed high suprabasal expression of cytokeratin 13 and low expression of cytokeratin 10. All these results suggest that our model of oral mucosa using fibrin-agarose scaffolds show several similarities with native human oral mucosa.

A combined approach for the assessment of cell viability and cell functionality of human fibrochondrocytes for use in tissue engineering.

Temporo-mandibular joint disc disorders are highly prevalent in adult populations. Autologous chondrocyte implantation is a well-established method for the treatment of several chondral defects. However, very few studies have been carried out using human fibrous chondrocytes from the temporo-mandibular joint (TMJ). One of the main drawbacks associated to chondrocyte cell culture is the possibility that chondrocyte cells kept in culture tend to de-differentiate and to lose cell viability under in in-vitro conditions. In this work, we have isolated human temporo-mandibular joint fibrochondrocytes (TMJF) from human disc and we have used a highly-sensitive technique to determine cell viability, cell proliferation and gene expression of nine consecutive cell passages to determine the most appropriate cell passage for use in tissue engineering and future clinical use. Our results revealed that the most potentially viable and functional cell passages were P5-P6, in which an adequate equilibrium between cell viability and the capability to synthesize all major extracellular matrix components exists. The combined action of pro-apoptotic (TRAF5, PHLDA1) and anti-apoptotic genes (SON, HTT, FAIM2) may explain the differential cell viability levels that we found in this study. These results suggest that TMJF should be used at P5-P6 for cell therapy protocols.

IL-17F co-expression improves cell growth characteristics and enhances recombinant protein production during CHO cell line engineering.

The generation of a high productivity cell line is a critical step in the production of a therapeutic protein. Many innovative engineering strategies have been devised in order to maximize the expression rate of production cells for increased process efficiency. Less effort has focused on improvements to the cell line generation process, which is typically long and laborious when using mammalian cells. Based on unexpected findings when generating stable CHO cell lines expressing human IL-17F, we studied the benefit of expressing this protein during the establishment of production cell lines. We demonstrate that IL-17F expression enhances the rate of selection and overall number of selected cell lines as well as their transgene expression levels. We also show that this benefit is observed with different parental CHO cell lines and selection systems. Furthermore, IL-17F expression improves the efficiency of cell line subcloning processes. IL-17F can therefore be exploited in a standard manufacturing process to obtain higher productivity clones in a reduced time frame.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen