A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms

This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.

[1]  Masaharu Munetomo,et al.  Linkage Identification by Fitness Difference Clustering , 2006, Evolutionary Computation.

[2]  Christopher D. Clack,et al.  gLINC: identifying composability using group perturbation , 2006, GECCO.

[3]  Masaharu Munetomo,et al.  Population Sizing of Dependency Detection by Fitness Difference Classification , 2005, FOGA.

[4]  Masaharu Munetomo,et al.  Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms , 2004, GECCO.

[5]  Masaharu Munetomo,et al.  Modeling Dependencies of Loci with String Classification According to Fitness Differences , 2004, GECCO.

[6]  David E. Goldberg,et al.  Dependency Structure Matrix Analysis: Offline Utility of the Dependency Structure Matrix Genetic Algorithm , 2004, GECCO.

[7]  David E. Goldberg,et al.  Convergence Time for the Linkage Learning Genetic Algorithm , 2004, Evolutionary Computation.

[8]  Alden H. Wright,et al.  Efficient Linkage Discovery by Limited Probing , 2003, Evolutionary Computation.

[9]  Dong-il Seo,et al.  A Hybrid Genetic Algorithm Based on Complete Graph Representation for the Sequential Ordering Problem , 2003, GECCO.

[10]  Masaharu Munetomo,et al.  A Parallel Genetic Algorithm Based on Linkage Identification , 2003, GECCO.

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

[12]  David E. Goldberg,et al.  Tightness Time for the Linkage Learning Genetic Algorithm , 2003, GECCO.

[13]  David E. Goldberg,et al.  An Analysis of a Reordering Operator with Tournament Selection on a GA-Hard Problem , 2003, GECCO.

[14]  Dong-il Seo,et al.  A Survey on Chromosomal Structures and Operators for Exploiting Topological Linkages of Genes , 2003, GECCO.

[15]  Byung Ro Moon,et al.  Toward minimal restriction of genetic encoding and crossovers for the two-dimensional Euclidean TSP , 2002, IEEE Trans. Evol. Comput..

[16]  Shigeyoshi Tsutsui,et al.  Probabilistic Model-Building Genetic Algorithms in Permutation Representation Domain Using Edge Histogram , 2002, PPSN.

[17]  Dirk Thierens,et al.  Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations , 2002, PPSN.

[18]  David E. Goldberg,et al.  Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm , 2002, PPSN.

[19]  Byung-Ro Moon,et al.  Voronoi quantized crossover for traveling salesman problem , 2002 .

[20]  Byung Ro Moon,et al.  Neuron Reordering For Better Neuro-genetic Hybrids , 2002, GECCO.

[21]  W. A. Greene,et al.  A Genetic Algorithm With Self-distancing Bits But No Overt Linkage , 2002, GECCO.

[22]  Byung Ro Moon,et al.  A Hybrid Genetic Algorithm For The Vehicle Routing Problem With Time Windows , 2002, GECCO.

[23]  Yong-Hyuk Kim,et al.  Genetic Search For Fixed Channel Assignment Problem With Limited Bandwidth , 2002, GECCO.

[24]  James Smith,et al.  On Appropriate Adaptation Levels for the Learning of Gene Linkage , 2002, Genetic Programming and Evolvable Machines.

[25]  Masaharu Munetomo,et al.  Linkage identification based on epistasis measures to realize efficient genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[26]  Pedro Larraanaga,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[27]  Adam Prügel-Bennett,et al.  Modeling crossover-induced linkage in genetic algorithms , 2001, IEEE Trans. Evol. Comput..

[28]  Dirk Thierens,et al.  Crossing the road to efficient IDEAs for permutation problems , 2001 .

[29]  G. Üçoluk,et al.  A building block favoring reordering method for gene positions in genetic algorithms , 2001 .

[30]  Paul J. Kennedy,et al.  A double-stranded encoding scheme with inversion operator for genetic algorithms , 2001 .

[31]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[32]  C.-Y. Lee,et al.  Adaptive evolvability via non-coding segment induced linkage , 2001 .

[33]  R. Santana,et al.  The mixture of trees Factorized Distribution Algorithm , 2001 .

[34]  David E. Goldberg,et al.  Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm , 2000, PPSN.

[35]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[36]  David E. Goldberg,et al.  OMEGA - Ordering Messy GA: Solving Permutation Problems with the Fast Genetic Algorithm and Random Keys , 2000, GECCO.

[37]  Peter Bock,et al.  Intelligent Recombination Using Individual Learning in a Collective Learning Genetic Algorithm , 2000, GECCO.

[38]  Hitoshi Iba,et al.  Controlling Effective Introns for Multi-Agent Learning by Genetic Programming , 2000, GECCO.

[39]  Byung-Ro Moon,et al.  The natural crossover for the 2D Euclidean TSP , 2000 .

[40]  Hillol Kargupta,et al.  A perspective on the foundation and evolution of the linkage learning genetic algorithms , 2000 .

[41]  David E. Goldberg,et al.  Linkage Identification by Non-monotonicity Detection for Overlapping Functions , 1999, Evolutionary Computation.

[42]  Jordan B. Pollack,et al.  Incremental commitment in genetic algorithms , 1999 .

[43]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[44]  Dirk Thierens,et al.  Linkage Information Processing In Distribution Estimation Algorithms , 1999, GECCO.

[45]  Anabela Simões,et al.  Transposition versus crossover: an empirical study , 1999 .

[46]  James R. Levenick,et al.  Swappers: introns promote flexibility, diversity and invention , 1999 .

[47]  K. Mehrotra,et al.  Linkage crossover for genetic algorithms , 1999 .

[48]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

[49]  Annie S. Wu,et al.  Putting More Genetics into Genetic Algorithms , 1998, Evolutionary Computation.

[50]  H. A. Mayer,et al.  ptGAsGenetic Algorithms Evolving Noncoding Segments by Means of Promoter/Terminator Sequences , 1998, Evolutionary Computation.

[51]  Yun-Sik Lee,et al.  GEORG: VLSI circuit partitioner with a new genetic algorithm framework , 1998, J. Intell. Manuf..

[52]  Sanghamitra Bandyopadhyay,et al.  Further Experimentations on the Scalability of the GEMGA , 1998, PPSN.

[53]  Kanta Premji Vekaria,et al.  Selective Crossover in Genetic Algorithms: An Empirical Study , 1998, PPSN.

[54]  Shumeet Baluja,et al.  Fast Probabilistic Modeling for Combinatorial Optimization , 1998, AAAI/IAAI.

[55]  Gang Wang,et al.  Revisiting the GEMGA: scalable evolutionary optimization through linkage learning , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[56]  Byung Ro Moon,et al.  GRCA: a hybrid genetic algorithm for circuit ratio-cut partitioning , 1998, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[57]  Kishan G. Mehrotra,et al.  Adaptive Linkage Crossover , 1998, Evolutionary Computation.

[58]  Byung-Ro Moon,et al.  Dynamic embedding for genetic VLSI circuit partitioning , 1998 .

[59]  Daniel L. Hartl,et al.  Genetics: Principles and Analysis , 1997 .

[60]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[61]  Shumeet Baluja,et al.  Genetic Algorithms and Explicit Search Statistics , 1996, NIPS.

[62]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

[63]  Peter J. Angeline,et al.  Two self-adaptive crossover operators for genetic programming , 1996 .

[64]  Hitoshi Iba,et al.  Extending genetic programming with recombinative guidance , 1996 .

[65]  H. Kargupta.  Search, polynomial complexity, and the fast messy genetic algorithm , 1996 .

[66]  Hillol Kargupta,et al.  Extending the class of order-k delineable problems for the gene expression messy genetic algorithm , 1996 .

[67]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[68]  Annie S. Wu,et al.  A Survey of Intron Research in Genetics , 1996, PPSN.

[69]  F. Oppacher,et al.  The benefits of computing with introns , 1996 .

[70]  Astro Teller,et al.  A study in program response and the negative effects of introns in genetic programming , 1996 .

[71]  Byung Ro Moon,et al.  Genetic Algorithm and Graph Partitioning , 1996, IEEE Trans. Computers.

[72]  Emanuel Falkenauer,et al.  A hybrid grouping genetic algorithm for bin packing , 1996, J. Heuristics.

[73]  Annie S. Wu,et al.  A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm , 1996, Evolutionary Computation.

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

[75]  Hillol Kargupta,et al.  The Gene Expression Messy Genetic Algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[76]  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.

[77]  David E. Goldberg,et al.  SEARCH, Blackbox Optimization, And Sample Complexity , 1996, FOGA.

[78]  Byung Ro Moon,et al.  On Multi-Dimensional Encoding/Crossover , 1995, ICGA.

[79]  Andrew B. Kahng,et al.  Toward More Powerful Recombinations , 1995, ICGA.

[80]  James R. Levenick.  Metabits: Generic Endogenous Crossover Control , 1995, ICGA.

[81]  Emanuel Falkenauer,et al.  Solving Equal Piles with the Grouping Genetic Algorithm , 1995, ICGA.

[82]  Annie S. Wu,et al.  Empirical Studies of the Genetic Algorithm with Noncoding Segments , 1995, Evolutionary Computation.

[83]  Jim Smith,et al.  An Adaptive Poly-Parental Recombination Strategy , 1995, Evolutionary Computing, AISB Workshop.

[84]  Kalyanmoy Deb,et al.  Sufficient conditions for deceptive and easy binary functions , 1994, Annals of Mathematics and Artificial Intelligence.

[85]  Annie S. Wu,et al.  Studies on the effect of non-coding segments on the genetic algorithm , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[86]  Byung Ro Moon,et al.  Analyzing Hyperplane Synthesis in Genetic Algorithms Using Clustered Schemata , 1994, PPSN.

[87]  Tony White,et al.  Adaptive Crossover Using Automata , 1994, PPSN.

[88]  Byung Ro Moon,et al.  A new genetic approach for the traveling salesman problem , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[89]  Emanuel Falkenauer,et al.  A New Representation and Operators for Genetic Algorithms Applied to Grouping Problems , 1994, Evolutionary Computation.

[90]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[91]  Byung Ro Moon,et al.  Hyperplane Synthesis for Genetic Algorithms , 1993, ICGA.

[92]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[93]  Dirk Thierens,et al.  Toward a Better Understanding of Mixing in Genetic Algorithms , 1993 .

[94]  Alain Delchambre,et al.  A genetic algorithm for bin packing and line balancing , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

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

[96]  K. Deb.  Binary and floating-point function optimization using messy genetic algorithms , 1991 .

[97]  D. E. Goldberg,et al.  An analysis of a reordering operator on a GA-hard problem , 1990, Biological Cybernetics.

[98]  L. D. Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, ICGA.

[99]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

[100]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

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

[102]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[103]  D. Goldberg,et al.  Evolutionary Algorithm Using Marginal Histogram Models in Continuous Domain , 2007 .

[104]  Hans-Georg Beyer,et al.  Self-Adaptation in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[105]  Ying-ping Chen,et al.  Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice , 2004 .

[106]  Ivo Everts,et al.  Extended Compact Genetic Algorithm , 2004 .

[107]  D. Goldberg,et al.  Probabilistic Model Building and Competent Genetic Programming , 2003 .

[108]  A. Townsend.  Genetic Algorithms – a Tutorial , 2003 .

[109]  박은종,et al.  Genetic search for fixed channel assignment problem with limited bandwidth , 2003 .

[110]  Dimitri Knjazew,et al.  OmeGA - a competent genetic algorithm for solving permutation and scheduling problems , 2002, Genetic algorithms and evolutionary computation.

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

[112]  T. P. Riopka,et al.  Intelligent recombination using genotypic learning in a collective learning genetic algorithm , 2002 .

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

[114]  Kumara Sastry,et al.  Efficient Cluster Optimization Using Extended Compact Genetic Algorithm With Seeded Population , 2001 .

[115]  Dirk Thierens,et al.  Advancing continuous IDEAs with mixture distributions and factorization selection metrics , 2001 .

[116]  Fernando G. Lobo,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[117]  Christopher D. Clack,et al.  Royal Road Encodings and Schema Propagation in Selective Crossover , 2000 .

[118]  P. Bosman,et al.  Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .

[119]  Fernando G. Lobo,et al.  Extended Compact Genetic Algorithm in C , 1999 .

[120]  G. Harik.  Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

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

[122]  Haynes.  Collective Adaptation: The Exchange of Coding Segments. , 1999, Evolutionary computation.

[123]  Emanuel Falkenauer,et al.  Applying Genetic Algorithms to Real-World Problems , 1999 .

[124]  M. Pelikán,et al.  The Bivariate Marginal Distribution Algorithm , 1999 .

[125]  Christopher D. Clack,et al.  Schema Propagation in Selective Crossover , 1999 .

[126]  Fernando G. Lobo,et al.  Linkage Learning Genetic Algorithm in C , 1998 .

[127]  Elizabeth W. Jones,et al.  Genetics:Principles and Analysis 4th ed , 1998 .

[128]  Fernando G. Lobo,et al.  Compressed introns in a linkage learning genetic algorithm , 1998 .

[129]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

[130]  Hillol Kargupta,et al.  SEARCH, Computational Processes in Evolution, and Preliminary Development of the Gene Expression Messy Genetic Algorithm , 1997, Complex Syst..

[131]  G. Harik.  Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms , 1997 .

[132]  Byung Ro Moon,et al.  A Two-Dimensional Embedding of Graphs for Genetic Algorithms , 1997, ICGA.

[133]  L. Merkle.  Analysis of linkage-friendly genetic algorithms , 1996 .

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

[135]  Peter J. Angeline,et al.  Explicitly Defined Introns and Destructive Crossover in Genetic Programming , 1996 .

[136]  Emanuel Falkenauer,et al.  Setting New Limits in Bin Packing with a Grouping GA Using Reduction , 1994 .

[137]  Larry J. Eshelman,et al.  Productive Recombination and Propagating and Preserving Schemata , 1994, FOGA.

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

[139]  Kalyanmoy Deb,et al.  Multimodal Deceptive Functions , 1993, Complex Syst..

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

[141]  Kalyanmoy Deb,et al.  Ordering Genetic Algorithms and Deception , 1992, PPSN.

[142]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[143]  Sushil J. Louis,et al.  Designer Genetic Algorithms: Genetic Algorithms in Structure Design , 1991, ICGA.

[144]  David E. Goldberg,et al.  MGA in C: A Messy Genetic Algorithm in C , 1991 .

[145]  James R. Levenick.  Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

[146]  L. Darrell Whitley,et al.  A Comparison of Genetic Sequencing Operators , 1991, ICGA.

[147]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale , 1990, Complex Syst..

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

[149]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction , 1989, Complex Syst..

[150]  David E. Goldberg,et al.  Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis , 1989, Complex Syst..

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

[152]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[153]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[154]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[155]  Daniel Raymond Frantz,et al.  Nonlinearities in genetic adaptive search. , 1972 .

[156]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[157]  R. Rosenberg.  Simulation of genetic populations with biochemical properties : technical report , 1967 .