Evolutionary Approach to Machine Learning and Deep Neural Networks

This chapter gives a basic introduction to evolutionary mechanisms and computation. We explain a fundamental theory of evolution and some debatable issues, such as how complex facilities like eyes have evolved and how to choose next generation from elite members. Thereafter, the method of evolutionary computation is described in details, followed by GP frameworks with several implementation schemes.

[1]  D. Nilsson,et al.  A pessimistic estimate of the time required for an eye to evolve , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[2]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[3]  Hitoshi Iba,et al.  An Evolutionary Computational Approach to Humanoid Motion Planning , 2012 .

[4]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[5]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[6]  H. Iba Bagging, Boosting, and bloating in Genetic Programming , 1999 .

[7]  Vinicius Veloso de Melo,et al.  Breast cancer detection with logistic regression improved by features constructed by Kaizen programming in a hybrid approach , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[8]  Dick den Hertog,et al.  Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming , 2009, IEEE Transactions on Evolutionary Computation.

[9]  Xiangji Huang,et al.  Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles , 2006, PAKDD.

[10]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

[11]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[12]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[13]  Vinicius Veloso de Melo,et al.  Kaizen programming , 2014, GECCO.

[14]  A. Bond,et al.  Visual predators select for crypticity and polymorphism in virtual prey , 2002, Nature.

[15]  Leonardo Vanneschi,et al.  Genetic programming needs better benchmarks , 2012, GECCO '12.

[16]  今井 正明,et al.  Kaizen (Ky'zen) : the key to Japan's competitive success , 1986 .

[17]  Vinicius Veloso de Melo,et al.  Predicting High-Performance Concrete Compressive Strength Using Features Constructed by Kaizen Programming , 2015, 2015 Brazilian Conference on Intelligent Systems (BRACIS).

[18]  A. Bond,et al.  Apostatic selection by blue jays produces balanced polymorphism in virtual prey , 1998, Nature.

[19]  Xin Yao,et al.  Classification-assisted Differential Evolution for computationally expensive problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[20]  Hitoshi Iba,et al.  Vanishing ideal genetic programming , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[21]  Vinicius Veloso de Melo,et al.  Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing , 2017, Neurocomputing.

[22]  Michael E. Tipping,et al.  Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .

[23]  Paulien Hogeweg,et al.  Evolutionary Consequences of Coevolving Targets , 1997, Evolutionary Computation.

[24]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[25]  Jordan B. Pollack,et al.  Automatic design and manufacture of robotic lifeforms , 2000, Nature.

[26]  Bruno Buchberger,et al.  The Construction of Multivariate Polynomials with Preassigned Zeros , 1982, EUROCAM.

[27]  G. A. Baker,et al.  THE THEORY AND APPLICATION OF THE PADE APPROXIMANT METHOD , 1964 .

[28]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[29]  Dervis Karaboga,et al.  Artificial bee colony programming for symbolic regression , 2012, Inf. Sci..

[30]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[31]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.