Method for simultaneously optimizing artificial neural network inputs and architectures using genetic algorithms

• US 20020059154A1
• Filed: 04/24/2001
• Published: 05/16/2002
• Est. Priority Date: 04/24/2000
• Status: Abandoned Application

• Current Assignee: XAIM Incorporated
• Original Assignee: XAIM Incorporated
• Inventors: Rodvold, David M.
• Application Number:
  US09/841,961
• Publication Number:
  US 20020059154A1
• Time in Patent Office: Days
• US Class Current: 706/26
• CPC Class Codes: G06N 3/082 modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

Artificial Neural Networks (ANNs) are useful mathematical constructs for tasks such as prediction and classification. While methods are well-established for the actual training of individual neural networks, determining optimal ANN architectures and input spaces is often a very difficult task. An exhaustive search of all possible combinations of parameters is rarely possible, except for trivial problems. A novel method is presented which applies Genetic Algorithms (GAs) to the dual optimization tasks of ANN architecture and input selection. The method contained herein accomplishes this using a single genetic population, simultaneously performing both phases of optimization. This method allows for a very efficient ANN construction process with minimal user intervention.

Claims

1. A process for selecting inputs and developing an architecture for an artificial neural network comprised of input neurons and hidden neurons, utilizing a genetic algorithm, and wherein each neural connection of said neural network is assigned one bit in a corresponding chromosome of said genetic algorithm, comprising the steps of:

constructing a population of chromosomes by arranging together on each of the chromosomes of said population contiguous groups of bits corresponding to neural connections associated with the input neurons of said neural network;

further developing said population of chromosomes by arranging together on each of the chromosomes of said population contiguous groups of bits corresponding to neural connections associated with the hidden neurons of said neural network;

assigning values to a first group of bits to allow selective elimination of an input neuron during application of said genetic algorithm;

assigning values to a second group of bits to allow selective elimination of a hidden neuron during application of said genetic algorithm;

calculating fitness of each chromosome in said population; and

evolving the population to further minimize connectivity of remaining neurons in the chromosomal representation.