How to successfully apply genetic algorithms in practice: Representation and parametrization

Evolutionary computation and genetic algorithms (GAs) in particular have been applied very successfully to many real world application problems. However, the success or failure of applying Genetic Algorithms is highly dependent on how a problem is represented. Additionally, the number of free parameters makes applying these methods a science of its own, presenting a huge barrier to entry for beginners. This tutorial will give a summary on various representational aspects, discuss parametrization and their influence on the dynamics of GAs.

[1]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

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

[3]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[4]  John Henry Holland.  Hierarchical descriptions, universal spaces and adaptive systems : technical report , 1968 .

[5]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[6]  Oliver Kramer,et al.  Evolutionary self-adaptation: a survey of operators and strategy parameters , 2010, Evol. Intell..

[7]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[8]  Alexander Asteroth,et al.  Evolving look ahead controllers for energy optimal driving and path planning , 2014, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings.

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

[10]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  Günter Rudolph,et al.  Evolutionary Strategies , 2012, Handbook of Natural Computing.

[13]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[14]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[15]  Lothar Thiele,et al.  A Comparison of Selection Schemes used in Genetic Algorithms , 1995 .

[16]  Alexander Asteroth,et al.  Evolution of optimal control for energy-efficient transport , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[17]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[18]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

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