Applications of GA and GP to Industrial Design Optimization and Inverse Problems

In this chapter the use of Genetic Algorithms and Genetic Programming for various industrial problems is discussed. Particular attention is paid to the case of difficult design optimization problems in which either (or both) (i) response functions are computationally expensive as well as affected by numerical noise and (ii) design variables are defined on a set of discrete variables.

[1]  Gerhard T. Suter,et al.  Diagonal Crack Width Control in Short Beams , 1971 .

[2]  Structural model verification with linear quadratic optimization theory , 1990 .

[3]  Vassili Toropov,et al.  Application of genetic programming and response surface methodology to optimization and inverse problems , 1998 .

[4]  M. Baruch.  Optimal correction of mass and stiffness matrices using measured modes , 1982 .

[5]  G. Matheron.  Principles of geostatistics , 1963 .

[6]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[7]  James K. Wight,et al.  Experimental Evaluation of Design Procedures for Shear Strength of Deep Reinforced Concrete Beams , 2002 .

[8]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[9]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[10]  Andrew Booker,et al.  Design and analysis of computer experiments , 1998 .

[11]  Ove Arup Partners.  The design of deep beams in reinforced concrete , 1977 .

[12]  Vassili Toropov,et al.  APPROXIMATION MODEL BUILDING FOR DESIGN OPTIMIZATION USING GENETIC PROGRAMMING METHODOLOGY , 1998 .

[13]  M. Baruch,et al.  Correction of stiffness matrix using vibration tests , 1982 .

[14]  David J. Murray-Smith,et al.  Structural system identification using genetic programming and a block diagram oriented simulation tool , 1996 .

[15]  Johann Sienz,et al.  Formulation of the Optimal Latin Hypercube Design of Experiments Using a Permutation Genetic Algorithm , 2004 .

[16]  Wei Wang,et al.  Shear Strength of Reinforced Concrete Deep Beams , 1993 .

[17]  Wei Chen,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[18]  P E Regan.  RESEARCH ON SHEAR: A BENEFIT TO HUMANITY OR A WASTE OF TIME? , 1993 .

[19]  K. H. Tan,et al.  Shear Behavior of Large Reinforced Concrete Deep Beams and Code Comparisons , 1999 .

[20]  F. Kong,et al.  WEB REINFORCEMENT EFFECTS ON DEEP BEAMS , 1970 .

[21]  Philip Seliger.  Engineering design optimization , 2008 .

[22]  N. K. Subedi,et al.  Reinforced concrete deep beams some test results , 1986 .

[23]  Ashraf F. Ashour,et al.  Empirical modelling of shear strength of RC deep beams by genetic programming , 2003 .

[24]  Prabhat Hajela,et al.  Recent developments in damage detection based on system identification methods , 1990 .

[25]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[26]  Henry P. Wynn,et al.  Maximum entropy sampling , 1987 .

[27]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[28]  Vassili Toropov,et al.  Application of Advanced Optimization Techniques to Parameter and Damage Identification Problems , 2005 .

[29]  Raphael T. Haftka,et al.  Design and optimization of laminated composite materials , 1999 .

[30]  Thomas T. C. Hsu,et al.  Shear Strength Prediction for Deep Beams With Web Reinforcement , 1987 .

[31]  Arthur H. Nilson,et al.  Design of concrete structures , 1972 .

[32]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[33]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[34]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[35]  Ashraf F. Ashour,et al.  Shear Capacity of Reinforced Concrete Deep Beams , 2000 .

[36]  K. N. Smith,et al.  Shear Strength of Deep Beams , 1982 .

[37]  Vassili Toropov,et al.  Modelling and Approximation Strategies in Optimization — Global and Mid-Range Approximations, Response Surface Methods, Genetic Programming, Low / High Fidelity Models , 2001 .

[38]  Vassili Toropov,et al.  The use of simplified numerical models as mid-range approximations , 1996 .

[39]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[40]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[41]  R. Manuel,et al.  DEEP BEAM BEHAVIOR AFFECTED BY LENGTH AND SHEAR SPAN VARIATIONS , 1971 .

[42]  Vassili Toropov,et al.  Application of a genetic algorithm and derivative-based techniques to identification of damage in steel structures , 1998 .

[43]  R. Haftka,et al.  Response surface approximations for structural optimization , 1996 .

[44]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[45]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[46]  M. Keijzer,et al.  Dimensionally aware genetic programming , 1999 .

[47]  Raphael T. Haftka,et al.  Optimization and Experiments: A Survey , 1998 .

[48]  Ronald L. Iman,et al.  Risk methodology for geologic disposal of radioactive waste: small sample sensitivity analysis techniques for computer models, with an application to risk assessment , 1980 .

[49]  A. Kabe.  Stiffness matrix adjustment using mode data , 1985 .

[50]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[51]  S Teng,et al.  SHEAR STRENGTH OF REINFORCED AND PRESTRESSED CONCRETE DEEP BEAMS. PART I: CURRENT DESIGN METHODS AND A PROPOSED EQUATION , 1998 .

[52]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[53]  Kang Hai Tan,et al.  A STRUT-AND-TIE MODEL FOR DEEP BEAMS SUBJECTED TO COMBINED TOP-AND-BOTTOM LOADING , 1997 .

[54]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[55]  Jerome H. Friedman.  Multivariate adaptive regression splines (with discussion) , 1991 .

[56]  N. K. Subedi,et al.  REINFORCED CONCRETE DEEP BEAMS: A METHOD OF ANALYSIS. , 1988 .

[57]  G. D. Jeong,et al.  Assessment of structural damage from natural frequency measurements , 1993 .

[58]  F. J. Soeiro,et al.  Structural damage detection based on static and modal analysis , 1989 .

[59]  Andy J. Keane,et al.  Computational Approaches for Aerospace Design: The Pursuit of Excellence , 2005 .

[60]  F. Kong,et al.  Deep Beams with Inclined Web Reinforcement , 1972 .

[61]  V. Ramakrishnan,et al.  Ultimate Strength of Deep Beams in Shear , 1968 .

[62]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[63]  F. Kong.  Reinforced Concrete Deep Beams , 1990 .