A New Crossover Operator in Genetic Programming for Object Classification

The crossover operator has been considered ldquothe centre of the stormrdquo in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.

[1]  Walter F. Bischof,et al.  Machine Learning and Image Interpretation , 1997, Advances in Computer Vision and Machine Intelligence.

[2]  Vic Ciesielski,et al.  Texture classifiers generated by genetic programming , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  Christian W. Omlin,et al.  A hybrid system for signature verification , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[6]  Peter A. Whigham,et al.  Grammatically-based Genetic Programming , 1995 .

[7]  Walter Alden Tackett,et al.  Genetic Programming for Feature Discovery and Image Discrimination , 1993, ICGA.

[8]  Conor Ryan,et al.  Using context-aware crossover to improve the performance of GP , 2006, GECCO '06.

[9]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[10]  Mengjie Zhang,et al.  Classification Strategies for Image Classification in Genetic Programming , 2003 .

[11]  Brijesh Verma.  A neural network based technique to locate and classify microcalcifications in digital mammograms , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[12]  Walter Alden Tackett,et al.  Recombination, selection, and the genetic construction of computer programs , 1994 .

[13]  Jason M. Daida,et al.  Characterizing the dynamics of symmetry breaking in genetic programming , 2006, GECCO.

[14]  Jens Palsberg,et al.  Program Optimization for Faster Genetic Programming , 1998 .

[15]  Victor Ciesielski,et al.  Texture analysis by genetic programming , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[17]  Peter Nordin,et al.  Complexity Compression and Evolution , 1995, ICGA.

[18]  Daniel Howard,et al.  Target detection in SAR imagery by genetic programming , 1999 .

[19]  Peter Nordin,et al.  Programmatic compression of images and sound , 1996 .

[20]  Robert P. W. Duin,et al.  Feature extraction in shared weights neural networksDick , 1996 .

[21]  I. Guyon,et al.  Handwritten digit recognition: applications of neural network chips and automatic learning , 1989, IEEE Communications Magazine.

[22]  Jason M. Daida,et al.  Probing for limits to building block mixing with a tunably-difficult problem for genetic programming , 2005, GECCO '05.

[23]  Conor Ryan,et al.  The Boru Data Crawler for Object Detection Tasks in Machine Vision , 2002, EvoWorkshops.

[24]  Astro Teller,et al.  PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System , 1995 .

[25]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[26]  Victor Ciesielski,et al.  An evolutionary approach to training feedforward and recurrent neural networks , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[27]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[28]  Wolfgang Banzhaf,et al.  Meta-Evolution in Graph GP , 1999, EuroGP.

[29]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[30]  Mengjie Zhang,et al.  Multiclass Object Classification Using Genetic Programming , 2004, EvoWorkshops.

[31]  Bangalore S. Manjunath,et al.  Genetic Programming for Object Detection , 1996 .

[32]  Victor Ciesielski,et al.  Genetic Programming for Multiple Class Object Detection , 1999, Australian Joint Conference on Artificial Intelligence.

[33]  Jeng-Sheng Huang,et al.  Object recognition using genetic algorithms with a Hopfield's neural model , 1997 .

[34]  Wolfgang Banzhaf,et al.  Linear-Graph GP - A New GP Structure , 2002, EuroGP.

[35]  Christoph Stahl,et al.  Advanced automatic target recognition for police helicopter missions , 2000, SPIE Defense + Commercial Sensing.

[36]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[37]  Conor Ryan,et al.  A Less Destructive, Context-Aware Crossover Operator for GP , 2006, EuroGP.

[38]  Peter G. Korning,et al.  Training neural networks by means of genetic algorithms working on very long chromosomes , 1995, Int. J. Neural Syst..

[39]  Kevin J. Lang.  Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's , 1995, ICML.

[40]  Vic Ciesielski,et al.  Representing classification problems in genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[41]  Paul McIlroy,et al.  Exploring some Commercial Applications of Genetic Programming , 1995, Evolutionary Computing, AISB Workshop.

[42]  Ayanna M. Howard,et al.  A multi-stage neural network for automatic target detection , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[43]  Mats G. Nordahl,et al.  Stereoscopic Vision for a Humanoid Robot Using Genetic Programming , 2000, EvoWorkshops.

[44]  Peter J. Angeline,et al.  Extending Genetic Programming with Recombinative Guidance , 1996 .

[45]  Qiang Huang,et al.  Underwater target classification using wavelet packets and neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[46]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

[47]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[48]  Malur K. Sundareshan,et al.  Data fusion and tracking of complex target maneuvers with a simplex-trained neural network-based architecture , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[49]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

[50]  Shahab Sokhansanj,et al.  Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision and Neural Networks , 2005 .

[51]  Victor Ciesielski,et al.  Towards Genetic Programming for Texture Classification , 2001, Australian Joint Conference on Artificial Intelligence.

[52]  Jano I. van Hemert,et al.  A Comparison of Genetic Programming Variants for Data Classification , 1999, IDA.

[53]  Victor Ciesielski,et al.  Understanding Evolved Genetic Programs for a Real World Object Detection Problem , 2005, EuroGP.

[54]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[55]  Victor Ciesielski,et al.  A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming , 2003, EURASIP J. Adv. Signal Process..

[56]  Krister Wolff,et al.  Evolving 3D model interpretation of images using graphics hardware , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[57]  Mengjie Zhang,et al.  Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection , 2003, EvoWorkshops.

[58]  John R. Koza,et al.  Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming , 1993, ICGA.

[59]  C. Arús,et al.  Genetic Programming for classification of brain tumours from Nuclear Magnetic Resonance biopsy , 1996 .

[60]  Alice J. O'Toole,et al.  CATEGORIZATION AND IDENTIFICATION OF HUMAN FACE IMAGES BY NEURAL NETWORKS: A REVIEW OF THE LINEAR AUTOASSOCIATIVE AND PRINCIPAL COMPONENT APPROACHES , 1994 .

[61]  Mengjie Zhang,et al.  Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification , 2006, Pattern Recognit. Lett..

[62]  Jerzy W. Bala,et al.  Using Learning to Facilitate the Evolution of Features for Recognizing Visual Concepts , 1996, Evolutionary Computation.

[63]  Karl Benson,et al.  Evolving finite state machines with embedded genetic programming for automatic target detection , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[64]  I Martínez-Pérez,et al.  Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies , 1998, NMR in biomedicine.

[65]  R. Sarker,et al.  Fast Texture Segmentation using Genetic Programming , 2003 .

[66]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

[67]  Manuela M. Veloso,et al.  A Contolled Experiment: Evolution for Learning Difficult Image Classification , 1995, EPIA.

[68]  R. Poli.  Genetic programming for image analysis , 1996 .

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

[70]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[71]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[72]  Jason M. Daida,et al.  Genetic Programming for Automatic Target Classification and Recognition , 1998, Evolutionary Programming.

[73]  David Andre,et al.  Automatically defined features: the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them , 1994 .