Lamarckian memetic algorithms: local optimum and connectivity structure analysis

Memetic algorithms (MAs) represent an emerging field that has attracted increasing research interest in recent times. Despite the popularity of the field, we remain to know rather little of the search mechanisms of MAs. Given the limited progress made on revealing the intrinsic properties of some commonly used complex benchmark problems and working mechanisms of Lamarckian memetic algorithms in general non-linear programming, we introduce in this work for the first time the concepts of local optimum structure and generalize the notion of neighborhood to connectivity structure for analysis of MAs. Based on the two proposed concepts, we analyze the solution quality and computational efficiency of the core search operators in Lamarckian memetic algorithms. Subsequently, the structure of local optimums of a few representative and complex benchmark problems is studied to reveal the effects of individual learning on fitness landscape and to gain clues into the success or failure of MAs. The connectivity structure of local optimum for different memes or individual learning procedures in Lamarckian MAs on the benchmark problems is also investigated to understand the effects of choice of memes in MA design.

[1]  Geoffrey E. Hinton,et al.  How Learning Can Guide Evolution , 1996, Complex Syst..

[2]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[3]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[4]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[5]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[6]  Jean-Michel Renders,et al.  Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  W. Hart.  Adaptive global optimization with local search , 1994 .

[8]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[9]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[10]  E. Jablonka,et al.  Epigenetic Inheritance and Evolution: The Lamarckian Dimension , 1995 .

[11]  B. H. Gwee,et al.  Polyominoes tiling by a genetic algorithm , 1996, Comput. Optim. Appl..

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

[13]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[14]  H. Beyer.  An alternative explanation for the manner in which genetic algorithms operate. , 1997, Bio Systems.

[15]  Domenico Quagliarella,et al.  Airfoil and wing design through hybrid optimization strategies , 1998 .

[16]  D. Quagliarella,et al.  Airfoil and wing design through hybrid optimization strategies , 1998 .

[17]  Sigeru Omatu,et al.  Efficient Genetic Algorithms Using Simple Genes Exchange Local Search Policy for the Quadratic Assignment Problem , 2000, Comput. Optim. Appl..

[18]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[19]  Natalio Krasnogor,et al.  Studies on the theory and design space of memetic algorithms , 2002 .

[20]  Edmund K. Burke,et al.  Multimeme Algorithms for Protein Structure Prediction , 2002, PPSN.

[21]  Hans-Paul Schwefel,et al.  How to analyse evolutionary algorithms , 2002, Theor. Comput. Sci..

[22]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[23]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[24]  Peter Merz,et al.  Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms , 2004, Evolutionary Computation.

[25]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[26]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[27]  E. Borenstein,et al.  The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes , 2006, Journal of evolutionary biology.

[28]  Ryszard S. Michalski,et al.  The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems , 2006, GECCO.

[29]  Yun-Wei Shang,et al.  A Note on the Extended Rosenbrock Function , 2006, Evolutionary Computation.

[30]  Kai-Yew Lum,et al.  Max-min surrogate-assisted evolutionary algorithm for robust design , 2006, IEEE Transactions on Evolutionary Computation.

[31]  Peter Merz,et al.  Memetic algorithms for combinatorial optimization problems : fitness landscapes and effective search strategies , 2006 .

[32]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[33]  C. Houck,et al.  Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms , 2007 .

[34]  Ben Paechter,et al.  Finding Feasible Timetables Using Group-Based Operators , 2007, IEEE Transactions on Evolutionary Computation.

[35]  Bernhard Sendhoff,et al.  On the Adaptive Disadvantage of Lamarckianism in Rapidly Changing Environments , 2007, ECAL.

[36]  Natalio Krasnogor,et al.  A study on the design issues of Memetic Algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[37]  Hisao Ishibuchi,et al.  Special Issue on Memetic Algorithms , 2007, IEEE Trans. Syst. Man Cybern. Part B.

[38]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

[39]  Ernesto Costa,et al.  Multidimensional Knapsack Problem: A Fitness Landscape Analysis , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[40]  Yew-Soon Ong,et al.  A proposition on memes and meta-memes in computing for higher-order learning , 2009, Memetic Comput..

[41]  Arthur C. Sanderson,et al.  Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization , 2009 .

[42]  Hisao Ishibuchi,et al.  Special issue on emerging trends in soft computing: memetic algorithms , 2009, Soft Comput..

[43]  Bernhard Sendhoff,et al.  The Influence of Learning on Evolution: A Mathematical Framework , 2009, Artificial Life.

[44]  Jürgen Branke,et al.  Balancing Population- and Individual-Level Adaptation in Changing Environments , 2009, Adapt. Behav..

[45]  Arthur C. Sanderson,et al.  Parameter Adaptive Differential Evolution , 2009 .

[46]  Vincenzo Loia,et al.  Editorial to first issue , 2010, J. Ambient Intell. Humaniz. Comput..