EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems

Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an event tracker enhanced GEP, which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.

[1]  Peter Broomhead,et al.  EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Graham Kendall,et al.  A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems , 2015, IEEE Transactions on Cybernetics.

[3]  L. Teodorescu,et al.  Gene Expression Programming Approach to Event Selection in High Energy Physics , 2006, IEEE Transactions on Nuclear Science.

[4]  Maozhen Li,et al.  Optimizing Hadoop Performance for Big Data Analytics in Smart Grid , 2017 .

[5]  Henri Luchian,et al.  Symbolic regression on noisy data with genetic and gene expression programming , 2005, Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05).

[6]  Graham Kendall,et al.  Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems , 2015, IEEE Transactions on Evolutionary Computation.

[7]  L. Teodorescu,et al.  High energy physics data analysis with gene expression programming , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[8]  Yue Jiang,et al.  Parallel Niche Gene Expression Programming Based on General Multi-core Processor , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[9]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[10]  Weimin Xiao,et al.  Prefix Gene Expression Programming , 2005 .

[11]  Maozhen Li,et al.  Optimizing hadoop parameter settings with gene expression programming guided PSO , 2016 .

[12]  Jack Dongarra,et al.  MPI: The Complete Reference , 1996 .

[13]  Zhengwen Huang,et al.  Schema theory for gene expression programming , 2014 .

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

[15]  Lipo Wang,et al.  Gene expression programming for induction of finite transducer , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[16]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[17]  Florian Pappenberger,et al.  Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes , 2008 .

[18]  Changjie Tang,et al.  Time Series Prediction Based on Gene Expression Programming , 2004, WAIM.

[19]  V.I. Litvinenko,et al.  Combining Clonal Selection Algorithm and Gene Expression Programming for Time Series Prediction , 2005, 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[20]  Siamak Tavakoli,et al.  Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker) , 2013, IEEE Transactions on Knowledge and Data Engineering.

[21]  Lixin Ding,et al.  Asynchronous Distributed Parallel Gene Expression Programming Based on Estimation of Distribution Algorithm , 2008, 2008 Fourth International Conference on Natural Computation.

[22]  Weimin Xiao,et al.  Evolving accurate and compact classification rules with gene expression programming , 2003, IEEE Trans. Evol. Comput..

[23]  Changjun Jiang,et al.  Schema Theory-Based Data Engineering in Gene Expression Programming for Big Data Analytics , 2018, IEEE Transactions on Evolutionary Computation.

[24]  Pınar Tüfekci,et al.  Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods , 2014 .

[25]  Zhengwen Huang,et al.  Enhanced Gene Expression Programming for signal-background discrimination in particle physics , 2009 .

[26]  Stewart W. Wilson Classifier Conditions Using Gene Expression Programming , 2008, IWLCS.