Generation Of Genetic Algorithm Research Articles

Control of flow using genetic algorithm for a circular cylinder executing rotary oscillation

We propose here a new approach to optimally control incompressible viscous flow past a circular cylinder for drag minimization by rotary oscillation. The flow at Re = 15000 is simulated by solving 2D Navier–Stokes equations in stream function-vorticity formulation. High accuracy compact scheme for space discretization and four stage Runge–Kutta scheme for time integration makes such simulation possible. While numerical solution for this flow field has been reported using a fast viscous-vortex method, to our knowledge, this has not been done at such a high Reynolds number by computing the Navier–Stokes equation before. The importance of scale resolution, aliasing problem and preservation of physical dispersion relation for such vortical flows of the used high accuracy schemes [Sengupta TK. Fundamentals of computational fluid dynamics. Hyderabad, India: University Press; 2004] is highlighted. For the dynamic problem, a novel genetic algorithm (GA) based optimization technique has been adopted, where solutions of Navier–Stokes equations are obtained using small time-horizons at every step of the optimization process, called a GA generation. Then the objective functions is evaluated that is followed by GA determined improvement of the decision variables. This procedure of time advancement can also be adopted to control such flows experimentally, as one obtains time-accurate solution of the Navier–Stokes equation subject to discrete changes of decision variables. The objective function – the time-averaged drag – is optimized using a real-coded genetic algorithm [Deb K. Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley; 2001] for the two decision variables, the maximum rotation rate and the forcing frequency of the rotary oscillation. Various approaches to optimal decision variables have been explored for the purpose of drag reduction and the collection of results are self-consistent and furthermore match well with the experimental values reported in [Tokumaru PT, Dimotakis PE. Rotary oscillation control of a cylinder wake. J Fluid Mech 1991;224:77].

SU‐FF‐T‐350: Optimizing the MLC Apertures for Conformal Radiotherapy with a Hybrid Algorithm

Purpose: To develop a feasible technique to simultaneously optimize the shapes and weights of the MLC apertures for conformal radiotherapy planning. Method and Materials: The shapes of the MLC apertures are optimized using the genetic algorithm (GA). There are several individuals in each generation of GA, and each individual includes a group of apertures, which number is equal to the number of pre‐selected beams, i.e., each individual is a trial conformal plan. The initial aperture shapes are obtained by adding different margins to the target on Beam's‐eye‐view (BEV). The margins range from −1.0 cm (i.e., shrink) to 1.0 cm (i.e., expand), with a step of 0.2 cm. The apertures of all beams with 1.0 cm margin constitute the first individual, and the apertures with 0.8 cm margin constitute the second, etc. For the selected two individuals (parents), the crossover and mutation operation are applied to the corresponding two apertures of the two parents. After the loop of shape optimization with GA, the aperture weights are optimized with a conjugate gradient (CG) method. The shapes and weights are iteratively optimized. Results: A five‐beam conformal plan is used for a head‐and‐neck tumor case. In the manual plan the weights of all apertures are set to unit, and the shapes are obtained by adding a 0.5 cm margin to the target on the BEV of each beam. About 2 minutes are taken to find the optimized plan. Both the dose distributions on CT slices and the DVH curves demonstrate the improvement of the optimized plan. Conclusion: This work indicates that the proposed GA and CG‐based algorithm is feasible and efficient for the optimization of the shapes and weight of MLC apertures for conformal radiotherapy planning, and a better dose distribution could be achieved within clinically acceptable computation time.

Hybridization of Genetic Algorithm with Local Search in Multiobjective Function Optimization: Recommendation of GA then LS

It is well known that local search (LS) improves the performance of genetic algorithms (GA) in single objective optimization, and it has recently been reported that the hybridization of GA with LS is effective in multiobjective combinatorial optimization as well. In most studies of this kind, LS is applied to the solutions of each generation of GA, which is the scheme called ``GA with LS'' herein. Another scheme, in which LS is applied to the solutions obtained with GA, has also been studied, which is called ``GA then LS'' herein. It seems there is no consensus in the literature as to which scheme is better. The situation in the multibojective function optimization literature is even more unclear since the number of such studies in the field has been small. However, some argue that LS contributes marginally to improving the performance of GA in multiobjective function optimization. This paper, assuming that objective functions are differentiable, reveals the reasons why GA is not necessarily effective in finding solutions of high precision, and hence hybridizing it with LS is indeed effective in multiobjective function optimization. It also suggests that the hybridization scheme which maximally exploits both GA and LS is GA then LS. Experiments confirmed that GA is not suitable for obtaining solutions of high precision, and GA then LS performs better than GA and GA with LS on many benchmark problems.

Groundwater Remediation Design under Uncertainty Using Genetic Algorithms

In groundwater problems, there always is some uncertainty associated with appropriate values for aquifer parameters. Therefore an optimal remediation strategy identified by assuming a deterministic description of the system may not yield an optimal and feasible design. This work develops a robust genetic algorithm (GA) approach that takes into account the uncertainty of hydraulic conductivity values when determining the best remediation design possible. Within a generation of the robust GA, all designs are evaluated using the same realization of the heterogeneous hydraulic conductivity field, but the realizations vary between GA generations. Ongoing performance of the designs is measured and is used in the GA evolution process. While the robust GA is a multiple realization method, minimal additional computation effort over that of a basic GA is required to identify robust designs. The robust GA is applied to two cases of varying heterogeneity of an example contaminated aquifer remediated by a pump-and-tre...

Some thoughts on the use of sampled fitness functions for the multilevel Darwinist brain

When attempting to evolve models of the real world through the information obtained by interacting with it, we always come across the same problem: the fitness function for the problem, that is, the real world, can only be known in a sampled manner. In this article we study the effect of different parameters and algorithmic strategies when working with sampled fitness functions in evolutionary processes. The results presented here correspond to a study of the effect of the size of the Short Term Memory and the number of generations between updates for a generic genetic algorithm that will be operating within the Multilevel Darwinist Brain. From these results, several critical points may be considered in order to define the limits to which computations can be simplified when working with sampled fitness functions while maintaining the same representational power. We provide a study of different proposals for the construction of Short Term Memories and their replacement strategies in order to obtain the maximum information with the minimum use of resources.

Soft Computing Techniques for Rank Aggregation on the World Wide Web

Rank aggregation is the problem of generating a near-“consensus” ranking for a given set of rankings. When applied to the web, this finds applications in meta-searching, search engine comparison, spam fighting and word association techniques. The rank aggregation obtained by optimizing the Spearman footrule distance is called footrule optimal aggregation (FOA), and it also satisfies the Condorcet property. We find in literature a polynomial time algorithm to compute FOA for full lists. However, when collating the results of the search engines, the lists are almost invariably always the partial ones, as different search engines usually return non-overlapping lists of documents. The FOA for partial lists, however, is NP-hard. This NP-hard nature of partial footrule optimal aggregation problem (PFOA) motivates us to apply genetic algorithm (GA) for the PFOA problem. The GA based technique may take long to compute, but we propose to decide upon the number of generations of GA based on the time limit allowed by the user. We have also considered some “positional” methods, as they are linear in complexity. A classical positional method is the Borda's method. Since, fuzzy logic has been extensively studied in literature for arriving at consensus in group decision making, the adoption of some fuzzy techniques is also being investigated here for getting an improvement over the Borda's method. We have not only adopted and compared the classical fuzzy rank ordering techniques for web applications, but also proposed three novel techniques that outshine the existing techniques.

Developing a generic genetic algorithm

Genetic and evolutionary algorithms, inspired by biological processes, provide a technique for programs to "automatically" improve their parameters. We discuss the basics of the algorithms and introduce our own hybrid. The development of this hybrid and its application to a simplified problem, evolving the coefficients for the sine function in a Taylor series, presents opportunities for computer science education with respect to model-building, data structures, and language features. Students must decide upon the representation of the chief mechanisms of genetic algorithms: mutation to alter the values of parameters directly and crossover to vary the groupings of co-evolved parameters in order to break away from local fitness maxima. They must examine the meaning of fitness itself as well as make many other modeling decisions. Ada itself provides both challenges and advantages: linked-lists must be well understood to be updated in an object-oriented context and hard-typing produces mixed reactions in students used to C++, but generics provide a powerful way to generalize the algorithm and incorporating different problem domains.

Enhancement of binary QSAR analysis by a GA-based variable selection method

Binary quantitative structure–activity relationship (QSAR) is an approach for the analysis of high throughput screening (HTS) data by correlating structural properties of compounds with a “binary” expression of biological activity (1=active and 0=inactive) and calculating a probability distribution for active and inactive compounds in a training set. Successfully deriving a predictive binary or any QSAR model largely depends on the selection of a preferred set of molecular descriptors that can capture the chemico–biological interaction for a particular biological target. In this study, a genetic algorithm (GA) was applied as a variable selection method in binary QSAR analysis. This GA-based variable selection method was applied to the analysis of three diverse sets of compounds, estrogen receptor (ER) ligands, carbonic anhydrase II inhibitors, and monoamine oxidase (MAO) inhibitors. Out of a variable pool of 150 molecular descriptors, predictive binary QSAR models were obtained for all three sets of compounds within a reasonable number of GA generations. The results indicate that the GA is a very effective variable selection approach for binary QSAR analysis.

Modeling genetic algorithms with interacting particle systems

We present in this work a natural Interacting Particle System (IPS) approach for modeling and studying the asymptotic behavior of Genetic Algorithms (GAs). In this model, a population is seen as a distribution (or measure) on the search space, and the Genetic Algorithm as a measure valued dynamical system. This model allows one to apply recent convergence results from the IPS literature for studying the convergence of genetic algorithms when the size of the population tends to infinity.
 We first review a number of approaches to Genetic Algorithms modeling and related convergence results. We then describe a general and abstract discrete time Interacting Particle System model for GAs, and we propose a brief review of some recent asymptotic results about the convergence of the N -IPS approximating model (of finite N -sized-population GAs) towards the IPS model (of infinite population GAs), including law of large number theorems, ILp-mean and exponential bounds as well as large deviations principles.
 Finally, the impact of modeling Genetic Algorithms with our interacting particle system approach is detailed on different classes of generic genetic algorithms including mutation, cross-over and proportionate selection. We explore the connections between Feynman-Kac distribution flows and the simple genetic algorithm. This Feynman-Kac representation of the infinite population model is then used to develop asymptotic stability and uniform convergence results with respect to the time parameter.

Application of BCUT metrics and genetic algorithm in binary QSAR analysis.

The application of three-dimensional H-suppressed BCUT metrics (BCUTs) in binary QSAR analysis was investigated using carbonic anhydrase II inhibitors and estrogen receptor ligands as test cases. Variable selection was accomplished with a genetic algorithm (GA). Highly predictive binary QSAR models were obtained for both sets of compounds within 200 GA generations. The derived binary QSAR models were validated with two sets of compounds not included in the training sets. The results indicate that BCUTs are very useful molecular descriptors, and the genetic algorithm is a very efficient variable selection tool in binary QSAR analysis.

Multireservoir Systems Optimization Using Genetic Algorithms: Case Study

A genetic algorithm approach is presented for the optimization of multireservoir systems. The approach is demonstrated through application to a reservoir system in Indonesia by considering the existing development situation in the basin and two future water resource development scenarios. A generic genetic algorithm model for the optimization of reservoir systems has been developed that is easily transportable to any reservoir system. This generality is a distinct practical advantage of the genetic algorithm approach. A comparison of the genetic algorithm results with those produced by discrete differential dynamic programming is also presented. For each case considered in this study, the genetic algorithm results are very close to the optimum, and the technique appears to be robust. Contrary to methods based on dynamic programming, discretization of state variables is not required. Further, there is no requirement for trial state trajectories to initiate the search using a genetic algorithm. Model sensitivity and generalizations that can be drawn from this and earlier work by Wardlaw and Sharif are also considered.

On the effective coupling of optimization algorithms to solve inverse problems of electromagnetism

The hybrid optimization tool presented in this paper combines generational genetic algorithms (GA) and variable metric (VM) optimizers. Both GA and VM may deal with the same, parameterized description of the inverse problem and a switch from GA to VM is being made automatically. GA is used to locate a subdomain in the design variables space containing the global minimum of the objective function. This global minimum may be found then, quickly and precisely, with the deterministic optimizer. The crucial concern of the hybrid algorithm design is to switch from stochastic to deterministic algorithm is such a way as to ensure that the global solution will be found in the fastest way. The article is focused on the algorithm which is able to determine whether GA has already found the desired subdomain described above. This algorithm is based on the cluster analysis using seed points and density‐determined hyperspheres.

A generic genetic algorithm for generating beam weights.

A genetic algorithm for generating beam weights is described. The algorithm improves an objective measure of the dose distribution while respecting dose volume constraints placed on critical structures. The algorithm was used to select beam weights for treatment of abdominal tumors. Weights were selected for up to 36 beams. Dose volume limits were placed on normal organs and a dose inhomogeneity limit was placed on tumor. Volumes were represented as sets of several hundred discrete points. The algorithm searched for the beam weights that would make the minimum tumor dose as high as the constraints would allow. The results were checked using dose volume histograms with standard sized grids. Nineteen trials were created using six patient cases by changing the required field margin or allowed beam position in each case. The sampling of points was sufficiently dense to yield solutions that strictly satisfied the constraints when the prescribed dose was renormalized by a factor of less than 6%. The genetic algorithm supplied solutions in 49 min on average, and in a maximum time of 87 min. The randomized search does not guarantee optimality, but high tumor doses were obtained. An example is shown for which the solution of the genetic algorithm gave a minimum tumor dose 7 Gy higher than the solution given by a simulated annealing algorithm under the same set of constraints. The genetic algorithm can be generalized to admit nonlinear functions of the beam intensities in the objective or in the constraints. These can include tumor control and normal tissue complication probabilities. The genetic algorithm is an attractive procedure for assigning beam weights in multifield plans. It improves the dose distribution while respecting specified rules for tissue tolerance.

Tackling Car Sequencing Problems Using a Generic Genetic Algorithm

The car sequencing problem (CarSP) was seen as a challenge to artificial intelligence. The CarSP is a version of the job-shop scheduling problem, which is known to be NP-complete. The task in the CarSP is to schedule a given number of cars (of different types) in a sequence to allow the teams in each workstation on the assembly line to fit the required options (e.g. radio, sunroof) on the cars within the capacity of that workstation. In unsolvable problems, one would like to minimize the penalties associated with the violation of the capacity constraints. Previous attempts to tackle the problem either have been unsuccessful or have been restricted to solvable CarSPs only. In this paper, we report on promising results in applying a generic genetic algorithm, which we call GAcSP, to tackle both solvable and unsolvable CarSPs.