Memetic Algorithms: Parametrization and Balancing Local and Global Search

This is a preprint of a book chapter from the Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, ISBN 978-3-642-23246-6, Springer, edited by F. Neri, C. Cotta, and P. Moscato. It is devoted to the parametrization of memetic algorithms and how to find a good balance between global and local search.

[1]  Thomas Jansen,et al.  On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..

[2]  Marc Toussaint,et al.  A No-Free-Lunch Theorem for Non-Uniform Distributions of Target Functions , 2004 .

[3]  Thomas Jansen,et al.  Optimization with randomized search heuristics - the (A)NFL theorem, realistic scenarios, and difficult functions , 2002, Theor. Comput. Sci..

[4]  Frederick Ducatelle,et al.  Ant colony optimization and local search for bin packing and cutting stock problems , 2004, J. Oper. Res. Soc..

[5]  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).

[6]  Thomas Bartz-Beielstein,et al.  Sequential parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[7]  Günter Rudolph,et al.  How Mutation and Selection Solve Long-Path Problems in Polynomial Expected Time , 1996, Evolutionary Computation.

[8]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[9]  L. Darrell Whitley,et al.  An analysis of iterated local search for job-shop scheduling. , 2003 .

[10]  Dirk Sudholt,et al.  Hybridizing Evolutionary Algorithms with Variable-Depth Search to Overcome Local Optima , 2011, Algorithmica.

[11]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[12]  Jim Smith,et al.  Memetic Algorithms: The Polynomial Local Search Complexity Theory Perspective , 2008, J. Math. Model. Algorithms.

[13]  Christos H. Papadimitriou,et al.  Computational complexity , 1993 .

[14]  Kalyanmoy Deb,et al.  Long Path Problems , 1994, PPSN.

[15]  Kalyanmoy Deb,et al.  A Local Search Based Evolutionary Multi-objective Optimization Approach for Fast and Accurate Convergence , 2008, PPSN.

[16]  Thomas Jansen,et al.  Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods On the Choice of the Mutation Probability for the ( 1 + 1 ) EA , 2006 .

[17]  Carsten Witt,et al.  Runtime Analysis of the ( + 1) EA on Simple Pseudo-Boolean Functions , 2006, Evolutionary Computation.

[18]  Mihalis Yannakakis,et al.  How easy is local search? , 1985, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

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

[20]  Natalio Krasnogor,et al.  A Study on the use of ``self-generation'' in memetic algorithms , 2004, Natural Computing.

[21]  Uwe Aickelin,et al.  An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering , 2007, J. Oper. Res. Soc..

[22]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[23]  Emile H. L. Aarts,et al.  Theoretical aspects of local search , 2006, Monographs in Theoretical Computer Science. An EATCS Series.

[24]  Carsten Witt,et al.  Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions , 2006 .

[25]  Dirk Sudholt,et al.  Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search , 2008, ANTS Conference.

[26]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[27]  R. Belew,et al.  Evolutionary algorithms with local search for combinatorial optimization , 1998 .

[28]  David E. Goldberg,et al.  Designing Efficient Genetic and Evolutionary Algorithm Hybrids , 2005 .

[29]  Thomas Bartz-Beielstein,et al.  Experimental Research in Evolutionary Computation - The New Experimentalism , 2010, Natural Computing Series.

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

[31]  David E. Goldberg,et al.  Optimizing Global-Local Search Hybrids , 1999, GECCO.

[32]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[33]  Ferrante Neri,et al.  An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[34]  Dirk Sudholt,et al.  The impact of parametrization in memetic evolutionary algorithms , 2009, Theor. Comput. Sci..

[35]  Tobias Storch,et al.  On the Choice of the Parent Population Size , 2008, Evolutionary Computation.

[36]  Ingo Wegener,et al.  Complexity theory - exploring the limits of efficient algorithms , 2005 .

[37]  Jim E. Smith,et al.  Coevolving Memetic Algorithms: A Review and Progress Report , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

[39]  Kenneth A. De Jong,et al.  Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods on the Choice of the Offspring Population Size in Evolutionary Algorithms on the Choice of the Offspring Population Size in Evolutionary Algorithms , 2004 .

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

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

[42]  Dirk Sudholt Local Search in Evolutionary Algorithms: The Impact of the Local Search Frequency , 2006, ISAAC.

[43]  Berthold Vöcking,et al.  Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP , 2007, SODA '07.

[44]  Carsten Witt,et al.  Population size versus runtime of a simple evolutionary algorithm , 2008, Theor. Comput. Sci..

[45]  Dirk Sudholt,et al.  On the analysis of the (1+1) memetic algorithm , 2006, GECCO.