Evolving Artificial Neural Networks for Medical Applications

Artiicial neural network (ANN) architecture design has been one of the most tedious and diicult tasks in ANN applications due to the lack of satisfactory and systematic methods of designing a near optimal architecture. Evolutionary algorithms have been shown to be very eeective in evolving novel ANN architectures for various problems. This paper proposes a new method for evolving ANN architectures and weights at the same time. The new method has been applied to four real-world data sets in the medical domain and achieved very good results. The traditional trial-and-error approach to designing ANNs has been replaced by an automatic evolutionary system which can nd a near optimal architecture and connection weights for a problem.

[1]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[2]  Richard S. Johannes,et al.  Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus , 1988 .

[3]  Michael C. Mozer,et al.  Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment , 1988, NIPS.

[4]  Yamashita,et al.  Backpropagation algorithm which varies the number of hidden units , 1989 .

[5]  M. V. Rossum,et al.  In Neural Computation , 2022 .

[6]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[7]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[8]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[9]  L. Darrell Whitley,et al.  GENITOR II: a distributed genetic algorithm , 1990, J. Exp. Theor. Artif. Intell..

[10]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[11]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[12]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[13]  Marcus Frean,et al.  The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks , 1990, Neural Computation.

[14]  D. Fogel.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[15]  Jocelyn Sietsma,et al.  Creating artificial neural networks that generalize , 1991, Neural Networks.

[16]  O. Mangasarian,et al.  Robust linear programming discrimination of two linearly inseparable sets , 1992 .

[17]  Wolfram Schiffmann,et al.  Synthesis and Performance Analysis of Multilayer Neural Network Architectures , 1992 .

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

[19]  Somnath Mukhopadhyay,et al.  A polynomial time algorithm for the construction and training of a class of multilayer perceptrons , 1993, Neural Networks.

[20]  Xin Yao,et al.  An empirical study of genetic operators in genetic algorithms , 1993, Microprocess. Microprogramming.

[21]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[22]  Asim Roy,et al.  An algorithm to generate radial basis function (RBF)-like nets for classification problems , 1995, Neural Networks.

[23]  Rudy Setiono,et al.  Use of a quasi-Newton method in a feedforward neural network construction algorithm , 1995, IEEE Trans. Neural Networks.

[24]  Xin Yao,et al.  A Preliminary Study on Designing Artiicial Neural Networks Using Co-evolution , 1995 .

[25]  Xin YaoComputational.  A Population-Based Learning Algorithm Which Learns BothArchitectures and Weights of Neural Networks , 1996 .