Different mutation and crossover set of genetic programming in an automated machine learning

Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm.  As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.

[1]  Suraya Masrom,et al.  Hybridization of Particle Swarm Optimization with adaptive genetic algorithm operators , 2013, 2013 13th International Conference on Intellient Systems Design and Applications.

[2]  Suresh Kumar,et al.  Routing in networks using genetic algorithm , 2018, Int. J. Commun. Networks Distributed Syst..

[3]  Mohammed Bennamoun,et al.  Deep feature learning for dummies: A simple auto-encoder training method using Particle Swarm Optimisation , 2017, Pattern Recognit. Lett..

[4]  P. Ranjit Jeba Thangaiah,et al.  An Improved Hybrid Feature Selection Method for Huge Dimensional Datasets , 2019, IAES International Journal of Artificial Intelligence (IJ-AI).

[5]  Randal S. Olson,et al.  Automating Biomedical Data Science Through Tree-Based Pipeline Optimization , 2016, EvoApplications.

[6]  Randal S. Olson,et al.  TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning , 2016, AutoML@ICML.

[7]  Masanori Suganuma,et al.  A genetic programming approach to designing convolutional neural network architectures , 2017, GECCO.

[8]  Aaron Klein,et al.  Efficient and Robust Automated Machine Learning , 2015, NIPS.

[9]  Randal S. Olson,et al.  Layered TPOT: Speeding up Tree-based Pipeline Optimization , 2017, AutoML@PKDD/ECML.

[10]  Suraya Masrom,et al.  Time-Varying Mutation in Particle Swarm Optimization , 2013, ACIIDS.

[11]  Sariffuddin Harun,et al.  A genetic algorithm based task scheduling system for logistics service robots , 2019 .

[12]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[13]  Randal S. Olson,et al.  Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science , 2016, GECCO.

[14]  Abdullah Al Mamun,et al.  Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization , 2009, Eur. J. Oper. Res..

[15]  S. Masrom,et al.  Dynamic parameterizations of particle swarm optimization and genetic algorithm for facility layout problem , 2017 .

[16]  Ismail Musirin,et al.  Simultaneous Network Reconfiguration and DG Sizing Using Evolutionary Programming and Genetic Algorithm to Minimize Power Losses , 2014 .

[17]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[18]  Lars Kotthoff,et al.  Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA , 2017, J. Mach. Learn. Res..

[19]  Venkatsai Siddesh Padala,et al.  Machine Learning: The New Language for Applications , 2019 .

[20]  Suraya Masrom,et al.  Dynamic parameterization of the particle swarm optimization and genetic algorithm hybrids for vehicle routing problem with time window , 2015, Int. J. Hybrid Intell. Syst..

[21]  Ali Idri,et al.  Improving Software Development effort estimating using Support Vector Regression and Feature Selection , 2019 .

[22]  Sharifah Azwa Shaaya,et al.  A multiple mitosis genetic algorithm , 2019 .

[23]  Syibrah Naim,et al.  Hybridization of Bat and Genetic Algorithm to Solve N-Queens Problem , 2018 .

[24]  Ismail. A. Humied.  Solving N-Queens Problem Using Subproblems based on Genetic Algorithm , 2018 .

[25]  Anil Kumar Malviya,et al.  Weather Forecasting Using Machine Learning Techniques , 2019 .

[26]  Nikhil R. Pal,et al.  A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification , 2016, IEEE Transactions on Cybernetics.