Dependency Structure Matrix Analysis: Offline Utility of the Dependency Structure Matrix Genetic Algorithm

This paper investigates the off-line use of the dependency structure matrix genetic algorithm (DSMGA). In particular, a problem-specific crossover operator is design by performing dependency structure matrix (DSM) analysis. The advantages and disadvantages of such an off-line use are discussed. Two schemes that helps the off-line usage are proposed. Finally, those off-line schemes are demonstrated by DSMGA on MaxTrap functions.

[1]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[2]  Hillol Kargupta,et al.  The performance of the gene expression messy genetic algorithm on real test functions , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[3]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[4]  Paul R. Carlile,et al.  Characterising Modular Architectures , 2002 .

[5]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[6]  Masaharu Munetomo,et al.  Identifying Linkage Groups by Nonlinearity/Non-monotonicity Detection , 1999 .

[7]  Reinhard Männer,et al.  Parallel Problem Solving from Nature — PPSN III , 1994, Lecture Notes in Computer Science.

[8]  David E. Goldberg,et al.  Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm , 2003, GECCO.

[9]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[10]  David E. Goldberg,et al.  Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .

[11]  Ali A. Yassine,et al.  Engineering design management: An information structure approach , 1999 .

[12]  P. Chongstitvatana,et al.  AUTOMATIC SYNTHESIS OF ROBOT PROGRAMS FOR A BIPED STATIC WALKER BY EVOLUTIONARY COMPUTATION , 2001 .

[13]  David E. Goldberg,et al.  Toward an Understanding of the Quality and Efficiency of Model Building for Genetic Algorithms , 2004, GECCO.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[16]  D. Goldberg,et al.  Analysis of Mixing in Genetic Algorithms: A Survey , 2002 .

[17]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[18]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[19]  David E. Goldberg,et al.  Learning Linkage , 1996, FOGA.

[20]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

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

[22]  D. V. Steward,et al.  The design structure system: A method for managing the design of complex systems , 1981, IEEE Transactions on Engineering Management.

[23]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[24]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[25]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[26]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[27]  David E. Goldberg,et al.  The Design of Innovation , 2002, Genetic Algorithms and Evolutionary Computation.

[28]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[29]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[30]  David E. Goldberg,et al.  A Genetic Algorithm for Developing Modular Product Architectures , 2003 .