| ### Chapter 11 - Genetic Algorithms and Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Russell D. Reed and Robert J. Marks II
Copyright © 1999 Massachusetts Institute of Technology

## 11.4 Remarks

The genetic algorithm is a general stochastic search method that has been used successfully in a number of ways for neural network design [3], [4], [50], [52], [66], [68], [108], [109], [155], [156], [304], [319]. Its main advantages are that it requires very little problem-specific information and is relatively insensitive to local maxima (minima). The algorithm itself is relatively easy to implement. Unlike some other search techniques, it does not require detailed problem-specific knowledge in order to generate new search candidates. Gradients are not required so the algorithm can be applied to discontinuous functions or functions that are defined empirically rather than analytically. It is applicable to mixed problems containing both continuous and discrete variables.

Its main disadvantage is the amount of processing required to evaluate and store a large number of different network configurations. Although the actual bit manipulations take very little time, the user-supplied objective function must be evaluated many times and this can be very slow with large networks and large training sets. It is worth noting, however, that the candidate solutions can be evaluated independently so N parallel processors should give close to a factor of N reduction in computation time.

Another caveat is that although the algorithm itself needs little problem-specific information, the efficiency of the search and the quality of the results depend heavily on how parameters are represented in the bit string. This is quite problem dependent and use of the algorithm may involve experimenting with several different representations to find one that works well.

Although there are claims of convergence to global maxima of the fitness function, convergence to local maxima is possible with small populations and aggressive culling of less successful solutions. Also, there are many parameters to be selected (population size, mutation rate, crossover method, fitness scaling, etc.) and it is not obvious how these affect the convergence properties.

Because the main theoretical advantage of the algorithm is its global optimization property and its main disadvantage is its inefficiency, it may be useful to use the algorithm in conjunction with more efficient local search methods. For example, the genetic algorithm might be used to do a coarsely quantized search to find the region containing the global minimum and then more efficient methods used to fine tune the solution in this region.