Multi-step GA for better results

GA (Genetic Algorithm) returns a little different optimization results each time GA runs, caused by its stochastic nature. How do we get better results which means the optimization result as close to the actual minimum/maximum as possible by GA? The paper presents a trial method, as a practical optimizing strategy for GA, called multi-step GA to get the better optimization result than that result calculated by GA running once. The multi-step GA introduces the two variable “maxStep” and “maxRound” as the new stopping conditions to specify the number of iterations so that GA can get better results in limited trial steps. It is proved that the method is an effective method for GA to increase accuracy generally by three different experiments regardless of optimization problems and varieties of GA. The method presented in this paper improves the accuracy of GA in whole or externally instead of improving the component parts of GA internally. It let the evolution of the world of the genome finish completely, and replays the world again and again to find the optimal result of that world.

[1]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[2]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

[3]  Uwe Aickelin,et al.  Tuning a multiple classifier system for side effect discovery using genetic algorithms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[4]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[5]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[6]  Zbigniew Michalewicz,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[7]  Hideki Satoh,et al.  味覚センサの出力データに基づく原料・ブレンド比の最適化;味覚センサの出力データに基づく原料・ブレンド比の最適化;Optimization of Food Ingredients and their Blend Ratios Based on Taste Sensor Output , 2015 .

[8]  Chuan-Kang Ting,et al.  On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection , 2005, ECAL.

[9]  Mohamed Tahar Kimour,et al.  Towards a Software Factory for Genetic Algorithms , 2014 .

[10]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[11]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[12]  Stephan M. Winkler,et al.  About the dynamics of essential genetic information: an empirical analysis for selected GA-variants , 2009, GEC '09.

[13]  Stephan M. Winkler,et al.  Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications , 2009 .

[14]  Gang Wang,et al.  Multi-strategic Approach of Fast Composition of Web Services , 2012, APWeb.

[15]  Alibakhsh Kasaeian,et al.  Modeling and optimization of an air-cooled photovoltaic thermal (PV/T) system using genetic algorithms , 2013 .

[16]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[17]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.