Maximizing Reliability with Task Scheduling In a Computational Grid Using GA

Simulated molecular evolution in a full combinatorial library

Genetic algorithms (GAs) are a stochastic search method for optimization problems based on the mechanics of natural selection and natural genetics (i.e., survival of the fittest). GAs have demonstrated considerable success in providing good solutions to many complex optimization problems and received more and more attentions during the past three decades. When the objective functions to be optimized in the optimization problems are multimodal or the search spaces are particularly irregular, algorithms need to be highly robust in order to avoid getting stuck at a local optimal solution. The advantage of GAs is just able to obtain the global optimal solution fairly. In addition, GAs do not require the specific mathematical analysis of optimization problems, which makes GAs themselves easily coded by users who are not necessarily good at mathematics and algorithms.KeywordsGenetic AlgorithmNash EquilibriumGenetic OperatorSoft ConstraintRoulette WheelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

chapter four - Evolutionary Computation Theory and Paradigms

Genetic Algorithms have become increasingly appreciated as an easy-to-use general method for a wide range of optimization problems. Their principle consists of maintaining and manipulating a population of solutions and implementing a ‘survival of the fittest’ strategy in their search for better solutions. In this chapter, GAs are combined with a signal subspace decomposition technique to enhance speech that is severely degraded by noise. To evaluate the effectiveness of this hybrid approach, a set of continuous speech recognition experiments is carried out by using the NTIMIT telephone speech database.

An automatic 3D non-rigid body registration system based upon the genetic algorithm (GA) process is presented. The system has been successfully applied to 2D and 3D situations using both rigid-body and affine transformations. Conventional optimization techniques and gradient search strategies generally require a good initial start location. The GA approach avoids the local minima/maxima traps of conventional optimization techniques. Based on the principles of Darwinian natural selection (survival of the fittest), the genetic algorithm has two basic steps: 1. Randomly generate an initial population. 2. Repeated application of the natural selection operation until a termination measure is satisfied. The natural selection process selects individuals based on their fitness to participate in the genetic operations; and it creates new individuals by inheritance from both parents, genetic recombination (crossover) and mutation. Once the termination criteria are satisfied, the optimum is selected from the population. The algorithm was applied on 2D and 3D magnetic resonance images (MRI). It does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. To evaluate the performance of the GA registration, the results were compared with results of the Automatic Image Registration technique (AIR) and manual registration which was used as the gold standard. Results showed that our GA implementation was a robust algorithm and gives very close results to the gold standard. A pre-cropping strategy was also discussed as an efficient preprocessing step to enhance the registration accuracy.

Genetic algorithms(GA), which are based on the idea of natural selection and survival of the fittest, have proven successful in solving complex problems that are not easily solved through conventional methods. Parallel genetic algorithm(PGA) is an extension of the classical GA. The important aspect in PGA is migration and GA operation. This paper presents PGAs that use fuzzy algorithm and natural select concept to migration of subpopulations. Fuzzy algorithm is used to change GA operators. Experimental results show that the proposed methods exhibit good performance compared to the classical method.

The genetic algorithm is a robust search and optimization technique based on the principles of natural genetics and survival of the fittest. Genetic algorithms (GA) are a promising new approach to global optimization problems, and are applicable to a wide variety of problems. HYPERGEN was developed as a research tool for investigating parallel genetic algorithms applied to combinatorial optimization problems. It provides the user with a wide variety of options to test the particular problem at hand. In addition, HYPERGEN is modular enough for a user to insert routines of his own for special needs, or for doing further research studies on parallel GAs. HYPERGEN was used successfully to find new 'best' tours on three 'standard' TSP problems, and out-performed a parallel simulated annealing algorithm on various package placement problems. The authors found it fairly easy to fine tune the parameters that drive a parallel GA for near optimal performance (population size, migration rate, and migration interval). >

Genetic algorithms, a stochastic research method, are Darwin’s “survival of the best.” Development of biological systems based on the principle of “survival of the fittest” emerged with the adaptation of the process to the computer environment. In genetic algorithms, processes executed are stored in computer memory similar to natural populations performed on units. Today, there are many linear and nonlinear solutions for optimization problems. The method has been developed. Designing optimization problems with these method variables is assumed to be continuous. But this acceptance does not always valid. In some problems, there are too many design variables and constraints for traditional optimization in solving such problems. The use of this method sometimes gives erroneous results or the solution time is too long. Since genetic algorithms are an intuitive method, a given may not find the optimum result for the problem. However, it optimizes the problems that cannot be solved by known methods or the solution time of the problem is too long. Initial nonlinear optimization genetic algorithms applied to the problems of traveling salesman, workshop scheduling, and syllabus preparation, such as complex optimization problems, have been successfully implemented.

Genetic algorithm is a non-numerical optimization method which based on natural selection and population genetics.Using genetic algorithm to optimize the mix proportion design of high performance concrete, it takes into account the economic profitability on the foundation of satisfying the requirements of durability, strength, workability and dimensional stability of concrete, it establishes a mathematic model applying the performance of material as constraint condition, and the economic cost as optimization target.Using binary coding to represent the chromosome bit serial of individual, through selection, crossover, mutation and other genetic operator to conduct global probability search, taking the principle of “survival of the fittest”, finally achieve the best population and individual. Compare the results of optimization with the mix proportion in practice engineering case, we can reach the conclusion that Genetic Algorithm could reduce the cost, save energy, provides better use value on engineering practice.

In this paper, the Genetic Algorithm (GA) is used to calculate the available transfer capability in an optimal power flow environment. This algorithm is inspired by the Darwin's principle of reproduction and survival of the fittest. GA uses simple operators, such as reproduction, crossover, and mutation. The proposed method has been applied to real Indian 246-bus power system with an objective to optimize ATC, keeping all constraints within specified limits. For comparison, the given system is tested with Simulated Annealing (SA) and Repeated Power Flow (RPF) methodology. The performances of both these methods are compared against GA to verify the robustness of the proposed GA algorithm.

??This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic Algorithm (GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize load generated load point outage in each sub-section. This approach is one of the most difficult procedures and become combination problems. A new approach using GA was developed for this problem. GA is a general purpose optimization technique based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. Test results for the model system with 24 nodes 29 branches are reported in the paper.

Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation problems over a period of generations, based upon the principles of natural selection and survival of the fittest. This paper describes an innovative tool to introduce the basics of the subject of Genetic Algorithms called GAVIn (Genetic Algorithms Visual Interface). It focuses on the domain of numerical function optimisation problems as these form a good basis for learning and operator comparison. The other problem domains are too varied and problem dependent to form a general, robust learning tool.

SA/GA is a genetic-algorithm-based approach to combinatorial optimization. It differs from ordinary genetic algorithms in that the acceptance of offspring is subject to the Metropolis sampling criterion [Metropolis 53] which is the basis of simulated annealing. The idea is that of a population subject to a climate that is getting colder. On one hand SA/GA can be viewed as a genetic algorithm with a crossover probability depending on the quality of the children relative to a decreasing temperature. On the other hand SA/GA can be modeled as a Markov chain with temperature-dependent transition probabilities [van Laarhoven 87]. When the population is set to one, the algorithm degenerates to a normal simulated annealing algorithm. We have tested the algorithm by applying it to a set of so- called genetic-algorithm-deceptive problems which are specially constructed to cause genetic algorithms to converge to sub-optimal results. SA/GA is not fooled by the deception and consistently finds the optimal solution. SA/GA also outperforms the pure simulated annealing algorithms while being less sensitive to parameter tuning.

This paper shows the application of Genetic Algorithm to Reentry trajectory optimization problem in the presence of constraints such as dynamic pressure and heat flux. The coupled non linear equations of motion of reentry body are difficult to solve using conventional methods like finding costate equations and then applying standard optimization methods. As an alternate, stochastic way of optimization through genetic algorithm is proposed. The results for a typical Reusable Launch Vehicle – Technology Demonstrator are in good match with that of Space Shuttle. Nomenclature CD = drag coefficient CDo = constant CL = lift Coefficient CLα = CL as a function of α, /rad D = drag force, N FT = thrust force, N go = gravity at earth surface, m/s 2 g = gravity at some altitude, m/s h = altitude, m Isp = specific impulse, N s/kg k = constant L = lift force, N LH = local horizontal plane M = total mass, kg Q = dynamic pressure, Pa q = heat flux, W/cm ro = radius of earth, m r = radial vector, m RN = nose radius, m S = area, m U = control vector V = velocity, m/s α = angle of attack, deg β = constant φ = latitude, deg γ = flight path angle, deg ρo = density at earth surface, kg/m ρ = density at some altitude,kg/m θ = longitude, deg σ = bank angle, deg ψ = heading angle, deg Introduction The concept of Reusable Launch Vehicle (RLV) is evolved in order to minimize cost per unit payload. Nearly 70% of cost of a launch vehicle lies in structure and avionics system, recovering the same and using it for further flights (after refurbishing) will be less expensive. The technologies like TPS material, structure and avionics system for a full fledged RLV are highly challenging. As a cost effective measure, a smaller version of RLV is proposed called as Technology Demonstrator (RLV-TD) as given in Fig.1 which will validate the above mentioned technologies in a step by step manner. Fig.1 RLV – Technology Demonstrator In the reentry trajectory design one has to ensure that with minimum fuel consumption the desired landing site is reached satisfying constraints such as dynamic pressure and heat flux. Traditionally reentry trajectory optimization is carried out by non dimensionalising the equations of motion and then solving it through gradient method using algorithms such as SGRA , Brysons. As the equations of motion of a reentry body are coupled non linear equation, it gives complicated co state equations which are difficult to solve and simulate. As an alternate, evolutionary approach is proposed for this type of problems. Genetic Algorithms(GA) are search algorithms based on the mechanism of natural selection. They lay on one of the most important principles of Darwin : survival of the fittest. Globally we make use of the population, submitted to many transformations. After some generations, the population endues no more; the best individual represents the optimal solution. GAs do not require 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization 4-6 September 2002, Atlanta, G orgia AIAA 2002-5466 Copyright © 2002 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. 2 American Institute of Aeronautics and Astronautics derivative or continuity of a function to be evaluated. They are simple, fast converging and easy to implement and simulate. Equations of motion of a reentry body The equations of motion for a lifting trajectory are given by (Fig.2) ψ γ φ φ ψ γ θ γ γ σ φ ψ γ ψ σ γ γ γ sin cos cos cos cos sin cos sin tan cos cos cos cos sin r V r V V r MV L r V MV L V g r V M F M D g V T = = = + − =   +   − = − − − =