Ecological coherence in marine reserve network design: an empirical evaluation of sequential site selection using genetic structure

Ecological coherence is a multifaceted conservation objective that includes some potentially conflicting concepts. These concepts include the extent to which the network maximises diversity (including genetic diversity) and the extent to which protected areas interact with non-reserve locations. To examine the consequences of different selection criteria, the preferred location to complement protected sites was examined using samples taken from four locations around each of two marine protected areas: Strangford Lough and Lough Hyne, Ireland. Three different measures of genetic distance were used: FST, Dest and a measure of allelic dissimilarity, along with a direct assessment of the total number of alleles in different candidate networks. Standardized site scores were used for comparisons across methods and selection criteria. The average score for Castlehaven, a site relatively close to Lough Hyne, was highest, implying that this site would capture the most genetic diversity while ensuring highest degree of interaction between protected and unprotected sites. Patterns around Strangford Lough were more ambiguous, potentially reflecting the weaker genetic structure around this protected area in comparison to Lough Hyne. Similar patterns were found across species with different dispersal capacities, indicating that methods based on genetic distance could be used to help maximise ecological coherence in reserve networks. ⺠Ecological coherence is a key component of marine protected area network design. ⺠Coherence contains a number of competing concepts. ⺠Genetic information from field populations can help guide assessments of coherence. ⺠Average choice across different concepts of coherence was consistent among species. ⺠Measures can be combined to compare the coherence of different network designs.

Non-genetic mechanisms communicating antibiotic resistance:rethinking strategies for antimicrobial drug design

Introduction: Infections by multidrug-resistant bacteria are of great concern worldwide. In many cases, resistance is not due to the presence of specific antibiotic-modifying enzymes, but rather associated with a general impermeability of the bacterial cell envelope. The molecular bases of this intrinsic resistance are not completely understood. Moreover, horizontal gene transfers cannot solely explain the spread of intrinsic resistance among bacterial strains. Areas covered: This review focuses on the increased intrinsic antibiotic resistance mediated by small molecules. These small molecules can either be secreted from bacterial cells of the same or different species (e.g., indole, polyamines, ammonia, and the Pseudomonas quinolone signal) or be present in the bacterial cell milieu, whether in the environment, such as indole acetic acid and other plant hormones, or in human tissues and body fluids, such as polyamines. These molecules are metabolic byproducts that act as infochemicals and modulate bacterial responses toward antibiotics leading to increasing or decreasing resistance levels. Expert opinion: The non-genetic mechanisms of antibiotic response modulation and communication discussed in this review should reorient our thinking of the mechanisms of intrinsic resistance to antibiotics and its spread across bacterial cell populations. The identification of chemical signals mediating increased intrinsic antibiotic resistance will expose novel critical targets for the development of new antimicrobial strategies.

Optimal design of cold-formed steel portal frames for stressed-skin action using genetic algorithm

This paper describes a stressed-skin diaphragm approach to the optimal design of the internal frame of a cold-formed steel portal framing system, in conjunction with the effect of semi-rigid joints. Both ultimate and serviceability limit states are considered. Wind load combinations are included. The designs are optimized using a real-coded niching genetic algorithm, in which both discrete and continuous decision variables are processed. For a building with two internal frames, it is shown that the material cost of the internal frame can be reduced by as much as 53%, compared with a design that ignores stressed-skin action.

An efficient genetic algorithm for the design optimization of cold-formed steel portal frame buildings

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

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.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Design of a Compact Genetic Microstrip Antenna with improved performance

In this paper the design issues of compact genetic microstrip antennas for mobile applications has been investigated. The antennas designed using Genetic Algorithms (GA) have an arbitrary shape and occupies less area (compact) compared to the traditionally designed antenna for the same frequency but with poor performance. An attempt has been made to improve the performance of the genetic microstrip antenna by optimizing the ground plane (GP) to have a fish bone like structure. The genetic antenna with the GP optimized is even better compared to the traditional and the genetic antenna.

A Faster 2D Technique for the design of Combinational Digital Circuits Using Genetic Algorithm

Combinational digital circuits can be evolved automatically using Genetic Algorithms (GA). Until recently this technique used linear chromosomes and and one dimensional crossover and mutation operators. In this paper, a new method for representing combinational digital circuits as 2 Dimensional (2D) chromosomes and suitable 2D crossover and mutation techniques has been proposed. By using this method, the convergence speed of GA can be increased significantly compared to the conventional methods. Moreover, the 2D representation and crossover operation provides the designer with better visualization of the evolved circuits. In addition to this, a technique to display automatically the evolved circuits has been developed with the help of MATLAB