Memetic Algorithms and Their Applications in Computer Science

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. Memetic algorithms are intrinsically concerned with exploiting all available knowledge about the problem under study. MAs are population-based metaheuristics. In this chapter we explore the applications of memetic algorithms to problems within the domains of image processing, data clustering and Graph coloring, i.e., how we can use the memetic algorithms in graph coloring problems, how it can be used in clustering based problems and how it is useful in image processing. Here, we discuss how these algorithms can be used for optimization problems. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimization.

[1]  Weiguo Sheng,et al.  Clustering with Niching Genetic K-means Algorithm , 2004, GECCO.

[2]  Hasin Al,et al.  Memetic Algorithm to Solve Graph Coloring Problem , 2013 .

[3]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[4]  Takashi Ikegami,et al.  Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata , 2010, Int. J. Nat. Comput. Res..

[5]  W. K. Hale.  Frequency assignment: Theory and applications , 1980, Proceedings of the IEEE.

[6]  D. Molodtsov.  Soft set theory—First results , 1999 .

[7]  Enrique Fernández-Blanco,et al.  Artificial Cell Systems Based in Gene Expression Protein Effects , 2009 .

[8]  Hichem Frigui,et al.  A Robust Competitive Clustering Algorithm With Applications in Computer Vision , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Zhen Yang,et al.  Effective Memetic Algorithms for VLSI Design = Genetic Algorithms Local Search Multi-Level Clustering , 2004, Evolutionary Computation.

[10]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Roman V. Yampolskiy,et al.  Genetic Algorithm Applied to the Graph Coloring Problem , 2012, MAICS.

[12]  B. K. Tripathy,et al.  Fuzzy Soft Set Theory and Its Application in Group Decision Making , 2016 .

[13]  B. K. Tripathy,et al.  A new approach to soft sets, soft multisets and their properties , 2015, Int. J. Reason. based Intell. Syst..

[14]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[15]  B. K. Tripathy,et al.  A New Approach to Fuzzy Soft Set Theory and Its Application in Decision Making , 2016 .

[16]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[17]  Bernd Freisleben,et al.  Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem , 1998, PPSN.

[18]  B.K. Tripathy,et al.  SDR: An algorithm for clustering categorical data using rough set theory , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[19]  Bah-Hwee Gwee,et al.  Solving four-colouring map problem using genetic algorithm , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[20]  A. Gamst,et al.  Some lower bounds for a class of frequency assignment problems , 1986, IEEE Transactions on Vehicular Technology.

[21]  Jack Sklansky,et al.  Feature Selection for Automatic Classification of Non-Gaussian Data , 1987, IEEE Transactions on Systems, Man, and Cybernetics.