Simulated Evolution and Learning

Over the last two decades, dynamic optimisation problems (DOPs) have become a challenging research topic. In DOPs, at least one part of the problem changes as time passes. These changes may affect the objective function(s) and/or constraint(s). In this paper, we propose and define a novel type of DOP in which dimensions change as time passes. It is called DOP with variable dimensions (DOPVD). We also propose a mask detection procedure to help algorithms in solving single objective unconstrained DOPVDs. This procedure is used to try to detect ineffective and effective dimensions while solving DOPVDs. In this paper, this procedure is added to Genetic Algorithms (GAs) to be tested. The results in this paper demonstrate that GAs which use the mask detection procedure outperform GA without it especially Periodic GA 5 (PerGA5).

[1]  Mengjie Zhang,et al.  Feature selection based on PSO and decision-theoretic rough set model , 2013, 2013 IEEE Congress on Evolutionary Computation.

[2]  Douglas B. Kell,et al.  Peptide detectability following ESI mass spectrometry: prediction using genetic programming , 2007, GECCO '07.

[3]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[4]  Edmund K. Burke,et al.  On improving genetic programming for symbolic regression , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[6]  Peter Nordin,et al.  Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets , 1996 .

[7]  Li-Yeh Chuang,et al.  Boolean binary particle swarm optimization for feature selection , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[8]  Nguyen Xuan Hoai,et al.  Where Should We Stop? An Investigation on Early Stopping for GP Learning , 2012, SEAL.

[9]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

[10]  Thomas J. Ostrand,et al.  \{PROMISE\} Repository of empirical software engineering data , 2007 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Ibrahim Kushchu,et al.  Genetic programming and evolutionary generalization , 2002, IEEE Trans. Evol. Comput..

[13]  Mukund Seshadri,et al.  Comprehensibility & Overfitting Avoidance in Genetic Programming for Technical Trading Rules , 2003 .

[14]  Mengjie Zhang,et al.  Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms , 2014, Appl. Soft Comput..

[15]  Hussein A. Abbass,et al.  The Role of Early Stopping and Population Size in XCS for Intrusion Detection , 2006, SEAL.

[16]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

[17]  Alexandros Agapitos,et al.  Early stopping criteria to counteract overfitting in genetic programming , 2011, GECCO '11.

[18]  Stephen G. MacDonell,et al.  What accuracy statistics really measure , 2001, IEE Proc. Softw..

[19]  Mengjie Zhang,et al.  A multi-objective particle swarm optimisation for filter-based feature selection in classification problems , 2012, Connect. Sci..

[20]  Cyril Fonlupt,et al.  Tarpeian Bloat Control and Generalization Accuracy , 2005, EuroGP.

[21]  Shahid Ali,et al.  Feature subset selection using multi-objective genetic algorithms , 2009, 2009 IEEE 13th International Multitopic Conference.

[22]  Tim W. Nattkemper,et al.  Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics , 2008, BMC Bioinformatics.

[23]  Mengjie Zhang,et al.  Binary PSO and Rough Set Theory for Feature Selection: a Multi-objective filter Based Approach , 2014, Int. J. Comput. Intell. Appl..

[24]  Concha Bielza,et al.  A review on probabilistic graphical models in evolutionary computation , 2012, Journal of Heuristics.

[25]  Alper Ekrem Murat,et al.  A discrete particle swarm optimization method for feature selection in binary classification problems , 2010, Eur. J. Oper. Res..

[26]  Helmut G. Katzgraber,et al.  Genetic braid optimization: A heuristic approach to compute quasiparticle braids , 2012, ArXiv.

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

[28]  Enrique Alba,et al.  Training Neural Networks with GA Hybrid Algorithms , 2004, GECCO.

[29]  Xin Wan,et al.  Constructing functional braids for low-leakage topological quantum computing , 2008, 0802.4213.

[30]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

[31]  Leonardo Vanneschi,et al.  Measuring bloat, overfitting and functional complexity in genetic programming , 2010, GECCO '10.

[32]  Lutz Prechelt,et al.  Early Stopping - But When? , 2012, Neural Networks: Tricks of the Trade.

[33]  Cyril Fonlupt,et al.  Exploring Overfitting in Genetic Programming , 2003, Artificial Evolution.

[34]  John Preskill,et al.  Topological Quantum Computation , 1998, QCQC.

[35]  N. Read,et al.  Beyond paired quantum Hall states: parafermions and incompressible states in the first excited Landau level , 1998, cond-mat/9809384.

[36]  Taghi M. Khoshgoftaar,et al.  Reducing overfitting in genetic programming models for software quality classification , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..

[37]  Xin Yao,et al.  journal homepage: www.elsevier.com/locate/infsof Ensembles and locality: Insight on improving software effort estimation , 2022 .

[38]  Sean Luke,et al.  Methods for Evolving Robust Programs , 2003, GECCO.

[39]  Concha Bielza,et al.  Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models , 2012, BMC Neuroscience.

[40]  Jean-Marc Desharnais,et al.  A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models , 1997, J. Syst. Softw..

[41]  Bart Baesens,et al.  Data Mining Techniques for Software Effort Estimation: A Comparative Study , 2012, IEEE Transactions on Software Engineering.

[42]  Chris Snijders.  Effects of risk preferences in social dilemmas: A game-theoretical analysis and evidence from two experiments , 2003 .

[43]  Magne Jørgensen,et al.  A Systematic Review of Software Development Cost Estimation Studies , 2007, IEEE Transactions on Software Engineering.

[44]  Leonardo Vanneschi,et al.  Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction , 2009, GECCO.

[45]  Pedro Larrañaga,et al.  Protein Folding in Simplified Models With Estimation of Distribution Algorithms , 2008, IEEE Transactions on Evolutionary Computation.

[46]  Maarten Keijzer,et al.  Improving Symbolic Regression with Interval Arithmetic and Linear Scaling , 2003, EuroGP.

[47]  Xin Yao,et al.  Software effort estimation as a multiobjective learning problem , 2013, TSEM.

[48]  Emilia Mendes,et al.  Why comparative effort prediction studies may be invalid , 2009, PROMISE '09.

[49]  N. Given.  Genetic Programming , Validation Sets , and Parsimony Pressure , 2005 .

[50]  Hui Li,et al.  Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.

[51]  Ferdinand Hergert,et al.  Improving model selection by nonconvergent methods , 1993, Neural Networks.

[52]  Barry W. Boehm,et al.  Software Engineering Economics , 1993, IEEE Transactions on Software Engineering.

[53]  Heejun Park,et al.  An empirical validation of a neural network model for software effort estimation , 2008, Expert Syst. Appl..

[54]  E A Nelson,et al.  MANAGEMENT HANDBOOK FOR THE ESTIMATION OF COMPUTER PROGRAMMING COSTS , 1967 .

[55]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

[56]  A. Griffin,et al.  Social semantics : altruism , cooperation , mutualism , strong reciprocity and group selection , 2007 .

[57]  Michael O'Neill,et al.  Improving the Generalisation Ability of Genetic Programming with Semantic Similarity based Crossover , 2010, EuroGP.

[58]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[59]  John E. Gaffney,et al.  Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation , 1983, IEEE Transactions on Software Engineering.

[60]  Mengjie Zhang,et al.  Novel Initialisation and Updating Mechanisms in PSO for Feature Selection in Classification , 2013, EvoApplications.

[61]  Xin Yao,et al.  Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing Problems , 2009, IEEE Transactions on Evolutionary Computation.

[62]  S. Subbotin,et al.  The multi objective evolutionary feature selection , 2008, 2008 International Conference on "Modern Problems of Radio Engineering, Telecommunications and Computer Science" (TCSET).

[63]  D. Kell,et al.  Explanatory optimization of protein mass spectrometry via genetic search. , 2003, Analytical chemistry.

[64]  Mengjie Zhang,et al.  New fitness functions in binary particle swarm optimisation for feature selection , 2012, 2012 IEEE Congress on Evolutionary Computation.

[65]  Matthew P. Evett,et al.  Preventing overfitting in GP with canary functions , 2005, GECCO '05.

[66]  D. Joanes,et al.  Comparing measures of sample skewness and kurtosis , 1998 .

[67]  JefI’rty C. Schlirrlrrer.  Beyond incremental processing : Tracking concept drift , 1999 .

[68]  S. Simon,et al.  Braid topologies for quantum computation. , 2005, Physical review letters.

[69]  Byoung-Tak Zhang,et al.  Balancing Accuracy and Parsimony in Genetic Programming , 1995, Evolutionary Computation.

[70]  Leonardo Vanneschi,et al.  Using crossover based similarity measure to improve genetic programming generalization ability , 2009, GECCO.

[71]  Conor Ryan,et al.  On Improving Generalisation in Genetic Programming , 2009, EuroGP.

[72]  L. Chuang,et al.  Chaotic maps in binary particle swarm optimization for feature selection , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.

[73]  J. Hintze,et al.  Violin plots : A box plot-density trace synergism , 1998 .

[74]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[75]  Nicholas S. Flann,et al.  Improving the accuracy and robustness of genetic programming through expression simplification , 1996 .

[76]  Ratna Babu Chinnam,et al.  mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification , 2011, Inf. Sci..