Subject: Review for IEEE TEC - TEVC-00312-2008
From: [email-masked]
To: [email-masked]
Cc: [email-masked]
Date: Sun, 28 Sep 2008 22:04:14 -0400 (EDT)

Dear Prof. Akira Imada ,

I have been asked to coordinate the review of a paper submitted to IEEE Transactions on Evolutionary Computation. It would be of great assistance to me if you could provide me with a review of this submission.

The review would need to be completed and submitted to manuscript central (the online peer review system) within the next 6 weeks. Please could you reply at your earliest convenience through the links below, indicating whether you will be able to review the paper or not?

The title and abstract of the paper follow below.

Thanking you in advance,

Prof. Mateen Rizki

To respond automatically, click below:

Unavailable: http://mc.manuscriptcentral.com/tevc-ieee?URL_MASK=Mf39yMPXdSkmjNhmf4cb

Agreed: http://mc.manuscriptcentral.com/tevc-ieee?URL_MASK=dSRsSCXj5y6MjTdmNkwS

Declined: http://mc.manuscriptcentral.com/tevc-ieee?URL_MASK=Zn4ks9m3tDQwrHs77nXZ

Here is the title of the manuscript:
Pattern Recalling Analysis of English Alphabets using Hopfield Model of Feedback Neural Network with Evolutionary searching

This is the abstract of the manuscript I would like for you to
review:
Abstract

In this paper we are analyzing the performance of Hopfield Model of feedback Neural Networks with general hebbian learning rule and Evolutionary Search (Genetic Algorithm) for Pattern Recognition. The training pattern set has been considered of the letters of English Alphabets. In the Hopfield type of neural networks, the weighted code of input patterns provides an auto-associative function in the network, which exhibits its associative memory feature. The objective is to determine the optimal weight matrix for efficient recalling of any approximate input pattern. For this, we explore the population generation technique (mutation and elitism), crossover and setting up of proper fitness evaluation functions to generate the new population of the weight matrices. This process will continue until the last weight matrix has been selected. The experiments consider a  neural network architecture that stores all letters of English alphabets (capitals only) using Hebbian rule and  then accomplishes the recalling of these stored patterns on presentation of any prototype input pattern of the already stored patterns using both - conventional hebbian rule and evolutionary algorithm. The evolutionary algorithm has been used to search for a near to optimal weight matrix for recalling of the stored pattern corresponding to the presented prototype and noisy input pattern. In most of the cases, the recalling of patterns using genetic algorithm seems to have better results than the conventional recalling with Hebbian rule. The simulated results demonstrate the better performance of network for recalling of the stored letters of English alphabets using genetic algorithm and minimize the randomness from the GA.  

Keywords: Evolutionary algorithms; Hopfield Neural Network; Hebbian learning rule; Genetic algorithm; Pattern recognition.