The Significance of Genetic Algorithms in Search, Evolution, Optimization and Hybridization: A Short Review

Evolutionary computing has facilitated numerous real life applications. Genetic algorithms are one of the pioneer method that works on principle of natural genetics to provide search and optimization facility. Apart from search and optimization, Genetic Algorithm provides evolutionary characteristics and hybridization with fuzzy logic and neural network. The paper explains general structure of Genetic Algorithm along with advantages of Genetic Algorithm. The paper represents multiple roles offered by Genetic Algorithm. Genetic Algorithm has been successful in developing numerous applications which includes machine learning and robotics, global and multi-objective optimization, classification, mathematical modeling, engineering and many more. The paper has significantly explains various roles presented by Genetic Algorithms by contributing to the development of evolutionary and intelligent hybrid systems.

[1]  Mukesh Kumar Rohil,et al.  Using Genetic Algorithm for Unit Testing of Object Oriented Software , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[2]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[3]  Miguel A. Salido,et al.  A Genetic Algorithm for Railway Scheduling Problems , 2008, Metaheuristics for Scheduling in Industrial and Manufacturing Applications.

[4]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[5]  Sanguthevar Rajasekaran,et al.  Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications , 2003 .

[6]  Marin Golub,et al.  Solving timetable scheduling problem using genetic algorithms , 2003, Proceedings of the 25th International Conference on Information Technology Interfaces, 2003. ITI 2003..

[7]  Jano I. van Hemert,et al.  Graph Coloring with Adaptive Evolutionary Algorithms , 1998, J. Heuristics.

[8]  Priti Srinivas Sajja,et al.  Measuring Human Intelligence by Applying Soft Computing Techniques: A Genetic Fuzzy Approach , 2013 .

[9]  Alex Fraser.  Simulation of genetic systems , 1962 .

[10]  Michael Affenzeller,et al.  Application of an Island Model Genetic Algorithm for a Multi-track Music Segmentation Problem , 2013, EvoMUSART.

[11]  Wei Li,et al.  Using Genetic Algorithm for Network Intrusion Detection , 2004 .

[12]  Hans J. Bremermann,et al.  Optimization Through Evolution and Recombination , 2013 .

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

[14]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[15]  El-Ghazali Talbi,et al.  A multiobjective genetic algorithm for radio network optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  Alex A. Freitas,et al.  Discovering comprehensible classification rules with a genetic algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[17]  Ilias P. Tatsiopoulos,et al.  A new hybrid parallel genetic algorithm for the job-shop scheduling problem , 2014, Int. Trans. Oper. Res..

[18]  Ester Bernadó-Mansilla,et al.  Evolving Fuzzy Rules with UCS: Preliminary Results , 2008, IWLCS.

[19]  William H. K. Lam.  Chapter 7 Genetic Algorithm-Based Approach for Transportation Optimization Problems , 2000 .

[20]  Sushil J. Louis,et al.  Towards the Co-Evolution of Influence Map Tree Based Strategy Game Players , 2006, 2006 IEEE Symposium on Computational Intelligence and Games.