Requirements controlled design: A method for discovery of discontinuous system boundaries in the requirements hyperspace

void update(); abstract boolean mutate(int probability); abstract void corrolateChromosomes(); public abstract Object clone() throws CloneNotSupportedException; } D.3.5 Simple Individual Class SimpleIndividual.java package edu.asdl.design.ea.basic; // // SimpleIndividual.java // JAVA SPEA // // Created by Peter Hollingsworth on Wed Sep 24 2003. // Copyright (c) 2003 __MyCompanyName__. All rights reserved. // import java.util.Random; public class SimpleIndividual extends Individual implements Cloneable { // // A more specific extension of the Idividual Class. It actually implements many Methods // public SimpleIndividual(short numgenes, short[] numbits) { bitChrome = new Chromosome(numgenes, numbits); bitChrome.initRandom(); realChrome = new RealValChromosome(bitChrome); this.numgenes = numgenes; this.numbits = numbits; } void corrolateChromosomes() { realChrome.update(); } public void update() { corrolateChromosomes(); } public boolean mutate(int probability) { // // Simple bit flip mutation // boolean bool= false; float prob = probability * 1e-2f; Random rand = new Random(); short ind1, ind2; float rand_val = rand.nextFloat(); if (rand_val <= prob) { ind1 = (short) rand.nextInt(numgenes); ind2 = (short)rand.nextInt(numbits[ind1]); bool = true; bitChrome.flipBit(ind1, ind2); } return bool; } public boolean crossover(int probability, SimpleIndividual tradeindv) { // //Simple Gene Swap Crossover // boolean bool= false; float prob = probability * 1e-2f; Random rand = new Random(); Gene trade_gene; Chromosome trade_chrom = tradeindv.bitChrome; float rand_val = rand.nextFloat(); if (rand_val <= prob) { short index = (short)rand.nextInt(bitChrome.getGeneNumber()); trade_gene = trade_chrom.gene[index]; trade_gene = bitChrome.tradeGene(index, trade_gene); bool = true; } return bool; } public boolean equals(SimpleIndividual simpind) { // // Checks if the two indivduals are Genetically Identical\ // boolean bool = true;

[1]  Hein Putter,et al.  On the effect of covariance function estimation on the accuracy of kriging predictors , 2001 .

[2]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[3]  S. Zacks,et al.  Applications of Catastrophe Theory for Statistical Modeling in the Biosciences , 1985 .

[4]  John J. Mahoney,et al.  Inlets for Supersonic Missiles , 1990 .

[5]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[6]  Dimitri N. Mavris,et al.  A Probabilistic Approach to Multivariate Constrained Robust Design Simulation , 1997 .

[7]  Christopher M. Bishop,et al.  Developments of the generative topographic mapping , 1998, Neurocomputing.

[8]  Dimitri N. Mavris,et al.  A Process for Tracking and Assessing Emerging Technology Development Programs for Resource Allocation , 2001 .

[9]  Dimitri N. Mavris,et al.  Economic Uncertainty Assessment Using a Combined Design of Experiments/Monte Carlo Simulation Approach with Application to an HSCT , 1995 .

[10]  Dimitri N. Mavris,et al.  A Method for Concept Exploration of Hypersonic Vehicles in the Presence of Open & Evolving Requirements , 2000 .

[11]  Ilan Kroo,et al.  Development and Application of the Collaborative Optimization Architecture in a Multidisciplinary Design Environment , 1995 .

[12]  John W. Murphy Catastrophe Theory: Implications for Probability , 1991 .

[13]  Fazle Hussain,et al.  Analysis of inviscid vortex breakdown in a semi-infinite pipe , 1998 .

[14]  John R. Olds,et al.  The suitability of selected multidisciplinary design and optimization techniques to conceptual aerospace vehicle design , 1992 .

[15]  Daniel P. Raymer,et al.  Aircraft Design: A Conceptual Approach , 1989 .

[16]  Manfred Opper,et al.  Sparse Representation for Gaussian Process Models , 2000, NIPS.

[17]  Matthias Seeger,et al.  Relationships between Gaussian processes, Support Vector machines and Smoothing Splines , 2000 .

[18]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[19]  Carlos A. Coello Coello,et al.  An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[20]  Rajeev Kumar,et al.  Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Genetic Algorithm , 2002, Evolutionary Computation.

[21]  D. Mackay,et al.  Introduction to Gaussian processes , 1998 .

[22]  Roger L. Jones,et al.  Approximation of L2-processes by Gaussian processes , 1998 .

[23]  Gert Vegter,et al.  The inverted pendulum : A singularity theory approach , 1999 .

[24]  Dimitri N. Mavris,et al.  Forecasting the Impact of Technology Infusion on Subsonic Transport Affordability , 1998 .

[25]  David Mackay,et al.  Gaussian Processes - A Replacement for Supervised Neural Networks? , 1997 .

[26]  Dimitri N. Mavris,et al.  A Technique for Use of Gaussian Processes in Advanced Meta-Modeling , 2003 .

[27]  Dimitri N. Mavris,et al.  A Stochastic Approach to Multi-disciplinary Aircraft Analysis and Design , 1998 .

[28]  Michelle R. Kirby,et al.  A Methodology for Technology Identification, Evaluation, and Selection in Conceptual and Prelimina , 2001 .

[29]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[30]  Erik S. Van Vleck,et al.  A Feedback Algorithm for Determining Search Parameters for Monte Carlo Optimization , 1998 .

[31]  Donald E. Grierson,et al.  Pareto‐Optimal Conceptual Design of the Structural Layout of Buildings Using a Multicriteria Genetic Algorithm , 1999 .

[32]  Dimitri N. Mavris,et al.  Adaptive Selection of Pareto Optimal Engine Technology Solution Sets , 2002 .

[33]  Dimitri N. Mavris,et al.  An Application of a Technology Impact Forecasting (TIF) Method to an Uninhabited Combat Aerial Vehicle , 1999 .

[34]  Dimitri N. Mavris,et al.  Integrated product/process design/development (IPPD) through robust design simulation - The key for affordable systems , 1995 .

[35]  Dimitri N. Mavris,et al.  A Technique for Selecting Emerging Technologies for a Fleet of Commercial Aircraft to Maximize R&D Investment , 2001 .

[36]  Dimitri N. Mavris,et al.  DEMONSTRATION OF A PROBABILISTIC TECHNIQUE FOR THE DETERMINATION OF AIRCRAFT ECONOMIC VIABILITY , 1997 .

[37]  Dimitri N. Mavris,et al.  Assessing the Simultaneous Impacts of Requirements, Vehicle Characteristics, and Technologies During Aircraft Design , 2001 .

[38]  Radford M. Neal Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.

[39]  C. A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[40]  M. Gibbs,et al.  Efficient implementation of gaussian processes , 1997 .

[41]  Dimitri N. Mavris,et al.  Simultaneous Assessment of Requirements and Technologies in Rotorcraft Design , 2000 .

[42]  Dimitri N. Mavris,et al.  AN INVESTIGATION OF METAMODELING TECHNIQUES FOR COMPLEX SYSTEMS DESIGN , 2002 .

[43]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[44]  Christopher A. Mattson,et al.  Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization , 2005 .

[45]  Daniel P. Schrage,et al.  Tiltrotor Performance Sensitivities for Multidisciplinary Wing Optimization , 1993 .

[46]  David G. Luenberger,et al.  Linear and Nonlinear Programming: Second Edition , 2003 .

[47]  Manfred Opper,et al.  General Bounds on Bayes Errors for Regression with Gaussian Processes , 1998, NIPS.

[48]  Julien Scharl,et al.  Formulation and implementation of a methodology for dynamic modeling and simulation in early aerospace design , 2001 .

[49]  T. Simpson A concept exploration method for product family design , 1998 .

[50]  Geoffrey E. Hinton,et al.  Evaluation of Gaussian processes and other methods for non-linear regression , 1997 .

[51]  Dimitri N. Mavris,et al.  Assessing the Impact of Mission Requirements, Vehicle Attributes, Technologies and Uncertainty in Rotorcraft System Design , 2002 .

[52]  Dimitri N. Mavris,et al.  IPPD Through Robust Design Simulation for an Affordable Short Haul Civil Tiltrotor , 1997 .

[53]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[54]  Daniel P. Schrage,et al.  A Multidisciplinary Design Optimization Approach to Sizing Stopped Rotor Configurations Utilizing Reaction Drive and Circulation Control , 1994 .

[55]  R. Thom Stabilité structurelle et morphogenèse , 1974 .

[56]  Tao Wang Global Optimization for Constrained Nonlinear Programming , 2000 .

[57]  Andrew Paul Baker,et al.  The role of mission requirements, vehicle attributes, technologies and uncertainty in rotorcraft system design , 2002 .

[58]  John B. Cullen,et al.  Decentralization in growth and decline: A catastrophe theory approach , 1994 .

[59]  Ole Winther,et al.  Efficient Approaches to Gaussian Process Classification , 1999, NIPS.

[60]  Joseph Duffy,et al.  Catastrophe Analysis of the Planar Two-Spring Mechanism , 1998, Int. J. Robotics Res..

[61]  Dimitri N. Mavris,et al.  Technology Identification, Evaluation, and Selection for Commercial Transport Aircraft , 1999 .

[62]  Dimitri N. Mavris,et al.  Implementation of a Technology Impact Forecast Technique on a Civil Tiltrotor , 1999 .

[63]  Achille Messac,et al.  Effective Generation of the Pareto Frontier: The Normalized Normal Constraint Method , 2002 .

[64]  Dimitri N. Mavris,et al.  Elements of an Emerging Virtual Stochastic Life Cycle Design Environment , 1999 .

[65]  Jay D. Martin,et al.  USE OF ADAPTIVE METAMODELING FOR DESIGN OPTIMIZATION , 2002 .

[66]  J. Barkley Rosser,et al.  From Catastrophe to Chaos: A General Theory of Economic Discontinuities , 1991 .

[67]  Oliver Bandte,et al.  A probabilistic multi-criteria decision making technique for conceptual and preliminary aerospace systems design , 2000 .

[68]  Dimitri N. Mavris,et al.  A Stochastic Design Approach for Aircraft Affordability , 1998 .

[69]  Matthias W. Seeger,et al.  Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers , 1999, NIPS.

[70]  Masahiro Shoji,et al.  Boiling curves – bifurcation and catastrophe , 2001 .

[71]  Dimitri N. Mavris,et al.  Identification of the Requirements Space Topology for a Rapid Response Strike System , 2001 .

[72]  Robert Gilmore,et al.  Catastrophe Theory for Scientists and Engineers , 1981 .

[73]  Andrew Scott,et al.  Comparison of Collaborative Optimization to Conventional Design Techniques for a Conceptual RLV , 2000 .

[74]  Michael F. Ashby,et al.  Grouping materials and processes for the designer: an application of cluster analysis , 2002 .

[75]  Jack D. Mattingly,et al.  Aircraft engine design , 1987 .

[76]  Christopher K. I. Williams,et al.  Modelling Frontal Discontinuities in Wind Fields , 2002 .

[77]  Dimitri N. Mavris,et al.  Integrating design and manufacturing for the high speed civil transport , 1993 .

[78]  Ilan Kroo,et al.  Implementation and Performance Issues in Collaborative Optimization , 1996 .

[79]  Michael F. Ashby,et al.  An investment methodology for materials , 2002 .

[80]  Dimitri N. Mavris,et al.  Technology Impact Forecasting for a High Speed Civil Transport , 1998 .

[81]  Kroo Ilan,et al.  Multidisciplinary Optimization Methods for Aircraft Preliminary Design , 1994 .

[82]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[83]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[84]  Dimitri N. Mavris,et al.  A formulation of metamodel implementation processes for complex systems design , 2002 .

[85]  Timo Hartmann,et al.  Chaos: A Program Collection for the PC , 1994 .

[86]  S. B. Hariz,et al.  Limit Theorems for the Non-linear Functional of Stationary Gaussian Processes , 2002 .

[87]  Dimitri N. Mavris,et al.  Determination of revolutionary requirements boundaries for a high-speed, airbreathing propulsion system , 2002 .

[88]  A. Ismail-Yahaya,et al.  Effective generation of the Pareto frontier using the Normal Constraint method , 2002 .

[89]  Amos Storkey,et al.  Efficient Covariance Matrix Methods for Bayesian Gaussian Processes and Hopfield Neural Networks , 1999 .

[90]  B. Blanchard,et al.  Life-cycle cost and economic analysis , 1991 .

[91]  Andy J. Keane,et al.  Problem solving environments in aerospace design , 2001 .

[92]  John R. Olds,et al.  Multidisciplinary Design Optimization Techniques for Branching Trajectories , 1998 .

[93]  John R. Olds,et al.  Multidisciplinary Optimization Techniques for Branching Trajectories , 1998 .

[94]  A. Messac,et al.  Concept Selection Using s-Pareto Frontiers , 2003 .

[95]  Daniel J. Svyantek,et al.  The illusion of certainty: A catastrophe model of decision framing , 1991 .

[96]  Manfred Opper,et al.  Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations , 2000, NIPS.

[97]  G. Zoutendijk,et al.  Methods of feasible directions : a study in linear and non-linear programming , 1960 .

[98]  John E. R. Staddon,et al.  Optima for animals , 1982 .

[99]  Dimitri N. Mavris,et al.  Analysis of Advanced Technology Impact on HSCT Engine Cycle Performance , 1999 .

[100]  I. A. Budianto,et al.  A collaborative optimization approach to design and deployment of a space based infrared system constellation , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[101]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[102]  Michelle Kirby,et al.  Forecasting Technology Uncertainty in Preliminary Aircraft Design , 1999 .

[103]  Thomas Spence Smith Catastrophies in interaction: a note on arousal-dependent discontinuities in attachment behavior , 1994 .

[104]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[105]  Dimitri N. Mavris,et al.  Methodology for Examining the Simultaneous Impact of Requirements, Vehicle Characteristics, and Technologies on Military Aircraft Design , 2000 .

[106]  Weng Tat Chan,et al.  Derivation of Pareto front with genetic algorithm and neural network , 2001 .

[107]  Christopher K. I. Williams Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[108]  David Cebon,et al.  Materials Selection in Mechanical Design , 1992 .

[109]  Francis J. Hale Introduction to Aircraft Performance, Selection, and Design , 1984 .

[110]  K. Nandakumar,et al.  Bifurcation phenomena of generalized newtonian fluids in curved rectangular ducts , 1986 .

[111]  Dimitri N. Mavris,et al.  Adaptive Selection of Engine Technology Solution Sets from a Large Combinatorial Space , 2001 .