An implementation of new selection strategies in a genetic algorithm – population recombination and elitist refinement

References

• Andrew , B. Truss optimization using genetic algorithms . Proceedings of genetic and evolutionary computation conference (GECCO 2004) . June 26–30 2004 , Seattle, Washington, USA. Berlin : Springer .
   Google Scholar
• Angelov , P. P. and Wright , J. A. A center-of-gravity-based recombination operator for genetic algorithms . Proceedings of 26th IEEE annual conference of the IEEE, Industrial Electronics Society (IECON2000) . October 22–28 , Nagoya, Japan. pp. 259 – 264 . Piscataway, NJ : IEEE .
   Google Scholar
• Bhuvaneshwaran , V. , Langari , R. and Temponi , C. 2006 . A genetic algorithm and fuzzy approach (GAFA) for constrained nonlinear optimization in design . International Journal for Computational Methods in Engineering Science and Mechanics , 7 ( 3 ) : 173 – 189 .
   Google Scholar
• Chakraborty , G. and Chakraborty , B. Rank and proximity based crossover (RPC) to improve convergence in genetic search . Proceedings of IEEE congress on evolutionary computation (CEC2005) . July 19–22 , Edinburgh, UK. pp. 1311 – 1316 . Munich : IEEE .
   Google Scholar
• Chang , M. Group selection and its application to constrained evolutionary optimization . June 24–27 , Newport Beach, CA. Proceedings of IEEE congress on evolutionary computation (CEC2003 , pp. 684 – 691 . Piscataway, NJ : IEEE .
   Google Scholar
• Christopher , B. and Ian , C. P. 2004 . Developments of the cluster oriented genetic algorithm (COGA) . Engineering Optimization , 36 ( 2 ) : 249 – 279 .
   Google Scholar
• Daoxiong , G. and Xiaogang , R. A new multi-parent recombination genetic algorithm . June 21–23 , Dalian, China. Proceedings of the 6th world congress on intelligent control and automation , pp. 2099 – 2103 . Piscataway, NJ : IEEE .
   Google Scholar
• Deb , K. 2003 . Multi-objective optimization using evolutionary algorithm , Chichester : John Wiley & Sons, Ltd .
   Google Scholar
• Dukkipati , A. and Murty , M. N. Selection by parts: ‘Selection in two episodes’ in evolutionary algorithms . Proceedings of IEEE congress on evolutionary computation (CEC2002) . May 12–17 , Honolulu, Hawali. pp. 657 – 662 . Piscataway, NJ : IEEE .
   Google Scholar
• Erick , C. 2001 . Migration policies, selection pressure, and parallel evolutionary algorithms . Journal of Heuristics , 7 ( 4 ) : 311 – 334 .
   Google Scholar
• Fisz , J. J. 2005 . Genetic algorithms optimization approach supported by the first-order derivative and Newton-Raphson methods: Application to fluorescence spectroscopy . Chemical Physics Letters , 407 ( 1–3 ) : 8 – 12 .
   Google Scholar
• Graff , M. , Poli , R. and Moraglio , A. Linear selection . Proceedings of IEEE congress on evolutionary computation (CEC2007) . September 25–28 , Singapore. pp. 2598 – 2605 .
   Google Scholar
• Haftka , R. T. and Gürdal , Z. 1993 . Elements of structural optimization , The Netherlands : Kluwer Academic Publishers .
   Google Scholar
• Hajela , P. and Lee , E. 1995 . Genetic algorithms in truss topological optimization . International Journal of Solids and Structures , 32 ( 22 ) : 3341 – 3357 .
   Web of Science ®Google Scholar
• Hamzacebi , C. 2008 . Improving genetic algorithms’ performance by local search for continuous function optimization . Applied Mathematics and Computation , 196 ( 1 ) : 309 – 317 .
   Google Scholar
• Huber , R. and Schell , T. 2002 . Mixed size tournament selection . Soft Computing , 6 : 449 – 455 .
   Google Scholar
• Hwang , S. F. and He , R. S. 2006 . Engineering optimization using a real-parameter genetic-algorithm-based hybrid method . Engineering Optimization , 38 ( 7 ) : 833 – 852 .
   Web of Science ®Google Scholar
• Lee , J. , Kim , S. and Kang , S. 2002 . Evolutionary fuzzy modeling in global approximate optimization . International Journal of Vehicle Design , 28 ( 4 ) : 339 – 355 .
   Google Scholar
• Li , J. P. , Balazs , M. E. and Parks , G. T. 2007 . Engineering design optimization using species-conserving genetic algorithms . Engineering Optimization , 39 ( 2 ) : 147 – 161 .
   Web of Science ®Google Scholar
• Li , N. , Gu , J. and Liu , B. A new genetic algorithm based on negative selection . Proceedings of international conference on machine learning and cybernetics . August 13–16 , Dalian, China. pp. 4297 – 4299 . Piscataway, NJ : IEEE .
   Google Scholar
• Liua , J. L. and Chena , C. M. 2009 . Improved intelligent genetic algorithm applied to long-endurance airfoil optimization design . Engineering Optimization , 41 ( 2 ) : 137 – 154 .
   Google Scholar
• Mak , K. L. , Lau , J. S.K. and Wei , C. 2005 . A Markov chain analysis of genetic algorithms with individuals having different birth and survival rates . Engineering Optimization , 37 ( 6 ) : 571 – 589 .
   Web of Science ®Google Scholar
• Michalewicz , Z. 1995 . Genetic algorithms + Data structures = Evolution programs , London : Springer-Verlag .
   Google Scholar
• Oh , I. , Lee , J. and Moon , B. 2004 . Hybrid genetic algorithms for feature selection . IEEE Transactions on Pattern Analysis and Machine Intelligence , 26 ( 11 ) : 1424 – 1437 .
   PubMedGoogle Scholar
• Pohlheim, H., 2005. GEATbx examples: Examples of objective functions [online]. GEATbx version 3.7 (Genetic and evolutionary algorithm toolbox for use with Matlab). Available from: http://www.geatbx.com [Accessed 30 October 2006].
   Google Scholar
• Rao , S. S. and Xiong , Y. 2005 . A hybrid genetic algorithm for mixed-discrete design optimization . Transactions of the ASME, Journal of Mechanical Design , 127 ( 6 ) : 1100 – 1112 .
   Web of Science ®Google Scholar
• Rodrigo , P. D.S. and Jun , S. O.F. Optimum truss design under failure constraints combining continuous and integer programming . Proceedings of international conference on engineering optimization (EngOpt 2008) . June 1–5 2008 , Rio de Janeiro. Brazil, , Berlin : Springer .
   Google Scholar
• Thierens , D. and Goldberg , D. Elitist recombination: An integrated selection recombination GA . Proceedings of the 1st IEEE conference on evolutionary computation . June 27–29 , Orlando, FL, USA. pp. 508 – 512 . Piscataway, NJ : IEEE .
   Google Scholar
• Vieira , D. G. 2005 . A hybrid approach combining genetic algorithm and sensitivity information extracted from a parallel layer perceptron . IEEE Transactions on Magnetics , 41 ( 5 ) : 1740 – 1743 .
   Google Scholar
• Wang , Y. 2006 . A new encoding based genetic algorithm for the Traveling Salesman Problem . Engineering Optimization , 38 ( 1 ) : 1 – 13 .
   Web of Science ®Google Scholar
• Yoshiaki , S. , Noriyuki , T. and Yukinobu , H. A fuzzy clustering based selection method to maintain diversity in genetic algorithms . IEEE congress on evolutionary computation . July 16–21 , Vancouver, Canada. pp. 3007 – 3012 . Piscataway, NJ : IEEE .
   Google Scholar
• Yuan , Q. , He , Z. and Leng , H. 2008 . A hybrid genetic algorithm for a class of global optimization problems with box constraints . Applied Mathematics and Computation , 197 ( 2 ) : 924 – 929 .
   Google Scholar