A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimise the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as simulated annealing and variable neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, genetic algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a controlled migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

[1]  Martijn C. Schut,et al.  Boosting Genetic Algorithms with Self-Adaptive Selection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[2]  Wafa' Slaibi Alsharafat,et al.  Adaptive Steady State Genetic Algorithm for scheduling university exams , 2010, ICN 2010.

[3]  Terence C. Fogarty,et al.  Varying the Probability of Mutation in the Genetic Algorithm , 1989, ICGA.

[4]  Fa-Chao Li,et al.  Study on convergence of self-adaptive and multi-population composite Genetic Algorithm , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[5]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[6]  Na Li,et al.  Optimal Design of Discrete Structure with Directed Mutation Genetic Algorithms , 2006, 2006 6th World Congress on Intelligent Control and Automation.

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

[8]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[9]  K. S. Tang,et al.  Genetic Algorithms: Concepts and Designs with Disk , 1999 .

[10]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[11]  Hu Shousong,et al.  Brief Stochastic optimal control and analysis of stability of networked control systems with long delay , 2003 .

[12]  Qixin Zhu,et al.  Stochastic optimal control and analysis of stability of networked control systems with long delay , 2003, Autom..

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

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

[15]  Gang Ju,et al.  A selective migration parallel multi-objective genetic algorithm , 2010, 2010 Chinese Control and Decision Conference.

[16]  Marc Gravel,et al.  Ensuring population diversity in genetic algorithms: A technical note with application to the cell formation problem , 2007, Eur. J. Oper. Res..

[17]  Bilin Aksun Güvenç,et al.  Robust Yaw Stability Controller Design and Hardware-in-the-Loop Testing for a Road Vehicle , 2009, IEEE Transactions on Vehicular Technology.

[18]  Paul Stewart,et al.  Improved decision support for engine-in-the-loop experimental design optimization , 2010 .

[19]  Robert G. Reynolds,et al.  Evolutionary computation: Towards a new philosophy of machine intelligence , 1997 .

[20]  Conor Ryan,et al.  Promoting diversity using migration strategies in distributed genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[21]  Chris Murphy,et al.  Dominance-Based Multiobjective Simulated Annealing , 2008, IEEE Transactions on Evolutionary Computation.

[22]  Take Nakama,et al.  Transition and convergence properties of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise , 2009, 2009 IEEE Congress on Evolutionary Computation.

[23]  Chen Lin,et al.  An Adaptive Genetic Algorithm Based on Population Diversity Strategy , 2009, 2009 Third International Conference on Genetic and Evolutionary Computing.

[24]  William M. Spears,et al.  Crossover or Mutation? , 1992, FOGA.

[25]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[26]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[27]  Jie Yuan,et al.  Interactive genetic algorithms with large population size , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[28]  Rolf Isermann,et al.  Hardware-in-the-loop simulation for the design and testing of engine-control systems , 1998 .

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

[30]  L. Booker.  Foundations of genetic algorithms. 2: L. Darrell Whitley (Ed.), Morgan Kaufmann, San Mateo, CA, 1993, ISBN 1-55860-263-1, 322 pp., US$45.95 , 1994 .

[31]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

[32]  Lai-Man Po,et al.  Novel Directional Gradient Descent Searches for Fast Block Motion Estimation , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[33]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[34]  A. J. Keane,et al.  Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness , 1995, Artif. Intell. Eng..

[35]  Makoto Sakamoto,et al.  Effects of String Length and Mutation Rate on Success Probability of Genetic Algorithm , 2009, 2009 Fifth International Conference on Natural Computation.

[36]  Carlos Alberto Conceição António,et al.  Self-adaptation in Genetic Algorithms applied to structural optimization , 2008 .

[37]  Terence C. Fogarty,et al.  A Comparative Study of Steady State and Generational Genetic Algorithms , 1996, Evolutionary Computing, AISB Workshop.

[38]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[39]  J.T. Alander,et al.  On optimal population size of genetic algorithms , 1992, CompEuro 1992 Proceedings Computer Systems and Software Engineering.

[40]  Nor Bahiah Hj. Ahmad,et al.  A comparative analysis of mining techniques for automatic detection of student's learning style , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[41]  Makoto Sakamoto,et al.  Effects of Population Size on Computational Performance of Genetic Algorithm on Multiplicative Landscape , 2007, Third International Conference on Natural Computation (ICNC 2007).

[42]  Sung Chul Oh,et al.  Evaluation of motor characteristics for hybrid electric vehicles using the hardware-in-the-loop concept , 2005, IEEE Transactions on Vehicular Technology.

[43]  Sam Kwong,et al.  Genetic Algorithms : Concepts and Designs , 1998 .

[44]  Carlos García-Martínez,et al.  Continuous Variable Neighbourhood Search Algorithm Based on Evolutionary Metaheuristic Components: A Scalability Test , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[45]  D. Thierens.  Adaptive mutation rate control schemes in genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[46]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.