The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.

Fractional dynamics of genetic algorithms using hexagonal space tessellation

The paper formulates a genetic algorithm that evolves two types of objects in a plane. The fitness function promotes a relationship between the objects that is optimal when some kind of interface between them occurs. Furthermore, the algorithm adopts an hexagonal tessellation of the two-dimensional space for promoting an efficient method of the neighbour modelling. The genetic algorithm produces special patterns with resemblances to those revealed in percolation phenomena or in the symbiosis found in lichens. Besides the analysis of the spacial layout, a modelling of the time evolution is performed by adopting a distance measure and the modelling in the Fourier domain in the perspective of fractional calculus. The results reveal a consistent, and easy to interpret, set of model parameters for distinct operating conditions.

Low dispersing species : population genetic processes in space and time : the genus "Trochulus" (Gastropoda: Hygromiidae) as a case study

RÉSUMÉ Une espèce est rarement composée d'une population unique. Parce que les individus ont des capacités de dispersion limitées et que les paysages sont des mosaïques d'habitats, la plupart des espèces sont plutôt composées de sous-populations connectées par la migration. Cette variation spatiale influence directement la distribution de la variabilité génétique dans et entre les populations. Durant ce travail, nous avons abordé certains des processus populationnels qui ont joué un rôle supposé dans l'apparition de nouvelles espèces au sein du genre Trochulus. Plus précisément, nous avons tenté d'évaluer les impacts respectifs de l'isolement passé (facteurs historiques) et présent (facteurs locaux). Nous avons d'abord pu montrer que les faibles capacités de dispersion des escargots terrestres ont directement influencé leur histoire évolutive à toutes les échelles spatiales et temporelles. En réduisant l'effet homogénéisant de la migration, une faible dispersion maintient dans les populations les traces génétiques d'évènements passés. A l'échelle de la distribution globale de Trochulus villosus, ces traces ont permis de reconstruire une histoire faite d'isolements et d'expansions de populations. En combinant des données génétiques avec une modélisation de la niche climatique passée, il a été possible de proposer un scénario significativement meilleur que toutes les hypothèses alternatives que nous avons testées. A l'échelle locale par contre, l'héritage historique est difficile à distinguer de la dynamique actuelle. Ce fut le cas des lignées mitochondriales du complexe sericeus-hispidus : les deux principales lignées étaient phylogénétiquement éloignées, avaient eu des démographies passées différentes et corrélaient avec des différences morphologiques. D'un autre côté, le flux de gène nucléaire était fort, contredisant l'idée de deux espèces cryptiques isolées reproductivement. Pour pouvoir conclure à la présence ou non de deux espèces, il nous a manqué des informations locales sur la dynamique des populations et les conditions écologiques que l'on trouve dans la région d'étude. Enfin, nous avons pu souligner que la connectivité entre populations d'escargots est soumise à la qualité des habitats et à leur organisation spatiale. Les escargots sont dépendants d'un habitat et s'y adaptent, comme l'indiquent la présence de «poils » uniquement sur la coquille d'espèces vivant dans des habitats humides ou la corrélation entre morphologie et habitat au sein du complexe sericeus-hispidus. Logiquement donc, les escargots migrent préférentiellement au travers d'habitats favorables comme l'a montré la réduction de flux de gènes au travers des prairies chez T. villosus (une espèce forestière). De ces données, nous pouvons supposer que les populations d'escargots en particulier, et des espèces à faible dispersion en général, ont de fortes chances d'être affectées par les changements climatiques, avec de probables implications pour leurs histoires évolutives. SUMMARY : Species rarely consists in a single population. Because individuals have limited dispersal abilities, because landscapes are habitat patchworks, most species are made of several subpopulations connected by migration. This spatial variation has consequences on the distribution of genetic diversity within and between populations, creating a structure among the populations. During the present work, we investigated some of the population processes assumed to have played an important role on the speciation within the genus Trochulus. More specifically, we questioned the respective impacts of past (historical factors) or present (local factors) population isolations. We first could show that the poor dispersal abilities of land snails have had profound impacts on their evolutionary histories at all spatial and temporal scales. Low dispersal maintains a strong signature of past events in the populations by minimising the homogenising effects of geneflow. At the scale of Trochulus villosus global distribution, they allowed to retrieve the detailed history of this species population isolations and expansions. Combining a large genetic dataset with paleo-climatic niche modelling ended up with a historical scenario significantly better than all traditional alternatives we tested. At local scale on the contrary, past events become difficult to tease apart from ongoing processes. This was the case for the divergent mitochondria) lineages within the sericeus-hispidus complex: the two principal lineages appeared to be phylogenetically distant, to have experienced different demographic histories and to correlate with morphological differences. On the other hand, nuclear (present day) geneflow was high, contradicting the idea of two reproductively isolated cryptic species. Information on the local population dynamics and environmental conditions are lacking to be able to decide whether past isolation has indeed resulted here in new species. Finally, we emphasised the importance of the habitat types present in a landscape as well as their spatial organisation for the population connectivity of land snails. These species are tightly dependent on a habitat and adapt to it as shown by thé occurrence of hair-like structures only in species living in humid environments or by the correlation between shell morphology and habitat in the sericeus-hispidus complex. As a result, land snails preferentially migrate through favourable habitats: Trochulus villosus, a forest species, had its geneflow significantly reduced across meadows. From these data, we can hypothesise that the populations of land snails in particular and of low dispersing species in general are likely to be strongly affected by the ongoing climate changes, with potential major consequences on their evolutionary histories.

Time and space: genetic structure of the common sea urchin paracentrotus lividus in the south-eastern iberian coast

Spatio-temporal variability in settlement and recruitment, high mortality during the first life-history stages, and selection may determine the genetic structure of cohorts of long-lived marine invertebrates at small scales. We conducted a spatial and temporal analysis of the common Mediterranean Sea urchin Paracentrotus lividus to determine the genetic structure of cohorts at different scales. In Tossa de Mar (NW Mediterranean), recruitment was followed over 5 consecutive springs (2006-2010). In spring 2008, recruits and two-year-old individuals were collected at 6 locations along East and South Iberian coasts separated from 200 to over 1,100 km. All cohorts presented a high genetic diversity based on a fragment of mtCOI. Our results showed a marked genetic homogeneity in the temporal monitoring and a low degree of spatial structure in 2006. In 2008, coupled with an abnormality in the usual circulation patterns in the area, the genetic structure of the southern populations studied changed markedly, with arrival of many private haplotypes. This fact highlights the importance of point events in renewing the genetic makeup of populations, which can only be detected through analysis of the cohort structure coupling temporal and spatial perspectives.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.

A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

Genetic algorithm based distance spectrum technique for performance union bound of space-time trellis coded OFDM

This paper derives the performance union bound of space-time trellis codes in orthogonal frequency division multiplexing system (STTC-OFDM) over quasi-static frequency selective fading channels based on the distance spectrum technique. The distance spectrum is the enumeration of the codeword difference measures and their multiplicities by exhausted searching through all the possible error event paths. Exhaustive search approach can be used for low memory order STTC with small frame size. However with moderate memory order STTC and moderate frame size the computational cost of exhaustive search increases exponentially, and may become impractical for high memory order STTCs. This requires advanced computational techniques such as Genetic Algorithms (GAS). In this paper, a GA with sharing function method is used to locate the multiple solutions of the distance spectrum for high memory order STTCs. Simulation evaluates the performance union bound and the complexity comparison of non-GA aided and GA aided distance spectrum techniques. It shows that the union bound give a close performance measure at high signal-to-noise ratio (SNR). It also shows that GA sharing function method based distance spectrum technique requires much less computational time as compared with exhaustive search approach but with satisfactory accuracy.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]

Analysis of small signal stability margins using genetic optimization

Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.

Genetic algorithms for design of liquid retaining structure

In this paper, genetic algorithm (GA) is applied to the optimum design of reinforced concrete liquid retaining structures, which comprise three discrete design variables, including slab thickness, reinforcement diameter and reinforcement spacing. GA, being a search technique based on the mechanics of natural genetics, couples a Darwinian survival-of-the-fittest principle with a random yet structured information exchange amongst a population of artificial chromosomes. As a first step, a penalty-based strategy is entailed to transform the constrained design problem into an unconstrained problem, which is appropriate for GA application. A numerical example is then used to demonstrate strength and capability of the GA in this domain problem. It is shown that, only after the exploration of a minute portion of the search space, near-optimal solutions are obtained at an extremely converging speed. The method can be extended to application of even more complex optimization problems in other domains.

Knowledge-based system on optimum design of liquid retaining structures with genetic algorithms

This paper delineates the development of a prototype hybrid knowledge-based system for the optimum design of liquid retaining structures by coupling the blackboard architecture, an expert system shell VISUAL RULE STUDIO and genetic algorithm (GA). Through custom-built interactive graphical user interfaces under a user-friendly environment, the user is directed throughout the design process, which includes preliminary design, load specification, model generation, finite element analysis, code compliance checking, and member sizing optimization. For structural optimization, GA is applied to the minimum cost design of structural systems with discrete reinforced concrete sections. The design of a typical example of the liquid retaining structure is illustrated. The results demonstrate extraordinarily converging speed as near-optimal solutions are acquired after merely exploration of a small portion of the search space. This system can act as a consultant to assist novice designers in the design of liquid retaining structures.

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.

Optimization of multi-stage amplifiers in deep-submicron CMOS using a distributed/parallel genetic algorithm

IEEE International Symposium on Circuits and Systems, pp. 724 – 727, Seattle, EUA