fig1 illustrates an example block diagram of a system 100 for optimizing a neural network 120 in accordance with this invention . the neural network 120 is illustrated as a learning system . when placed in the training mode , via selector 129 , the input vectors 111 that are applied to the neural network 120 are derived from a training input 102 a , via switch s 1 . an optional input processor 110 performs the formatting or processing requirements to convert the training input 102 a , and other inputs 101 and 103 a , into a suitable form 111 for processing by the neural network 120 . in response to the input vector 111 , the neural network 120 produces an output vector 121 , using conventional neural network propagation techniques . as discussed above , the resultant output vectors 121 are dependent upon the architecture of the network ( including the number of intermediate node layers between the input and output nodes , the number of nodes at each layer , and their interconnection ), as well as the nodal weights assigned to each node . as discussed above , u . s . pat . no . 5 , 140 , 530 “ genetic algorithm synthesis of neural networks ”, incorporated by reference herein , presents the use of a genetic algorithm to construct an optimized custom neural network architecture . additionally , copending u . s . patent application “ image classification using evolved parameters ”, u . s . ser . no . 09 / 343 , 649 , filed jun . 29 , 1999 for keith mathias , murali mani , j . david schaffer , ( disclosure 1504 - 1346 ), presents encodings of evolved traits that represent different neural network architecture attributes , such as the number of hidden nodal layers and nodes per layer , and is incorporated by reference herein . consistent with these referenced documents , the neural network 120 contains some fixed architectural features and constraints , as well as parameterized features that are modifiable by an external input . the evolutionary algorithm device 150 provides the one or more network architecture parameters 152 that affect the architecture of the neural network 120 . the evolutionary algorithm device 150 also provides one or more network initialization parameters 151 that affect the initial condition of the neural network 120 . in accordance with this invention , as discussed below , these network initialization parameters 151 are encoded in the traits of the sample neural network architectures that are presented to the neural network 150 for evaluation . the neural network device 120 uses the network initialization parameters 151 and network architecture parameters 152 provided by the evolutionary algorithm device 150 to create a neural network architecture for processing each of the input vectors 111 to produce each output vector 121 . each of a plurality of training inputs 102 a is applied to the current architecture of the neural network 150 in a sequential manner . as each output vector 121 is produced , it is compared , within the neural network device 120 , to an output vector value 102 b that a properly trained neural network device 120 should produce in response to the input 102 a being applied . using conventional feedback techniques , any differences , or errors , between the produced output vector 121 and the proper output vector 102 b are used to adjust the nodal weights within the neural network so as to reduce such errors . as noted above , an accumulated error factor , based on a batch of input vectors , is preferably used to adjust the nodal weights , and the entire process is iteratively repeated to further refine the nodal weights . a number of techniques can be applied to terminate the training cycle . a fixed number of iterations of the training inputs 102 a may be applied ; the training inputs 102 a can be applied until the magnitude of the errors reach a predetermined acceptable limit ; the application of training inputs 102 a may be terminated after a predetermined time limit ; or other termination tests , or combinations of tests , can be applied . in accordance with another aspect of this invention , the evolutionary algorithm device 150 also provides training parameters 153 that affect the training of the currently configured neural network architecture . the training parameters 153 may include , for example , the aforementioned training input quantity or duration limits , error thresholds , and the like . at the termination of the training cycle , the resultant trained neural network within the device 120 is evaluated , using evaluation inputs 103 a , via switch s 1 . as discussed above , the effectiveness of the training of the neural network , and the neural network itself , is dependent upon the initialization parameters 151 , the architecture parameters 152 , and the training parameters 153 that are provided to the neural network device 120 by the evolutionary algorithm device 150 . thus , an evaluation of the trained neural network provides a measure of effectiveness of the particular provided parameter set 151 , 152 , 153 . within the evolutionary algorithm device 150 , the effectiveness of the trained neural network is determined by applying a sequence of evaluation inputs 103 a and comparing the resultant output vectors 121 with corresponding proper evaluation output vectors 103 b . a composite measure of the differences between each output vector 121 and each proper output vector 103 b is associated with the parameter set 151 , 152 as a measure of effectiveness for the parameter set 151 , 152 , 153 . by applying different parameter sets 151 , 152 , 153 and repeating the above training and evaluation process , a measure of effectiveness can be determined for each of these different parameter sets 151 , 152 , 153 and the most effective parameter set 151 , 152 , 153 can be identified and selected for use in the neural network device 120 for processing new inputs 101 , inputs that do not have predetermined proper outputs , to produce new outputs 131 that are likely to be consistent with a proper output . that is , for example , if the neural network system 100 is trained and evaluated for classifying images , using training 102 a and evaluation 103 a input images that have corresponding proper output classifications 102 b and 103 b that are determined by a human expert , a new output classification 131 corresponding to a new input 101 can be expected to be consistent with a classification that the human expert would determine for the input 101 . a more effective neural network can be expected to provide a classification 131 that is more likely to be consistent with the result that the human expert would provide . fig2 illustrates an example flow diagram for determining and applying different parameter sets 151 , 152 , 153 to a neural network system 100 in a directed trial and error search for an optimal neural network system architecture . this directed trial and error search is effected via an evolutionary algorithm , as would be embodied in the evolutionary algorithm device 150 . an initial population 201 of sample parameter sets is created and stored as traits 211 of a current population 210 , using conventional evolutionary algorithm encoding techniques . each trait 211 typically corresponding to a gene or set of genes in a chromosome associated with each member of the population 210 . in contrast to conventional systems , the traits 211 of the population 210 include an encoding that provides at least one parameter 151 that affects the initial condition of the neural network 120 , or at least one parameter 153 that affects the training of the neural network 120 . for example , the encoding may specifically include randomly generated values for each nodal weight of the nodes in the neural network 120 , or , for ease of coding , may include a seed value that is used to initialize a random number generator that generates each nodal weight in the neural network 120 . in like manner , the encoding may be an index to a list of random values , or to a list of seeds for a random number generator . as is common in the art , a conventional random number generator , typically termed a pseudo - random number generator , repeatedly provides the same sequence of “ random ” numbers following an initialization with the same seed . in the illustrated example of fig2 the performance evaluator 220 includes the functions , such as the aforementioned pseudo - random number generator , required to transform the member traits 211 into the initialization parameters 151 , architecture parameters 152 , and training parameters 153 that are provided to the neural network 120 . the traits 211 of each member of the population 210 are provided to the performance evaluator 220 . the performance evaluator 220 provides the parameter set 151 , 152 , 153 to the neural network system 100 . as discussed above , the neural network system 100 configures and initializes a neural network 120 corresponding to the provided parameter set 151 , 152 , applies the input training vectors 102 a corresponding to the provided training parameters 153 , and adjusts the nodal weights of the configured neural network based on the proper output training vectors 102 b . after the neural network that corresponds to the provided parameter set 151 , 152 , 153 is trained , it is evaluated as discussed above by applying evaluation input vectors 103 a . the performance evaluator compares each output vector 121 corresponding to each evaluation input vector 103 a with each proper evaluation output vector 102 b , to form a measure of performance 221 associated with the particular parameter set 151 , 152 , 153 . because the parameter set 151 , 152 , 153 is derived from the particular member traits 211 , the measure of performance 221 is likewise associated to the member . each member &# 39 ; s traits 211 of the current population 210 are similarly processed so as to determine a measure of performance associated with each member of the current population 210 . a selector 240 selects the better performing members of the current population 210 to provide a set of the best performers 241 . a termination detector 250 determines whether to continue or terminate the search for an optimal neural network architecture , as discussed below . if the search is continued , the best performers 241 are provided to an offspring generator 260 . the offspring generator 260 generates a new population 261 based on the traits of the best performing members 241 . any one of a variety of techniques , common in the art of evolutionary algorithms , can be applied to generate the new population 261 , such as reproduction , pairwise recombination , mutation , and so on . depending upon the evaluation methods employed , the new population 261 may include both prior members of the population 210 as well as the newly generated offspring . if the same set of training 102 a and evaluation 103 a input vectors will be applied to each new current population 210 , then there is no need to evaluate a member more than once . if random or changed input vectors are used , the new population will preferably include the prior members who performed well with a different set of input vectors , so that a comparable measure of performance can be obtained at each generation . that is , to minimize the likelihood of one member performing better or worse than another because of the training or evaluation sets applied to each , all members that are directly compared to each other are compared based on the same set of training or evaluation sets . in a preferred embodiment of this invention , a random selection of training 102 a and evaluation 103 a input vectors are selected at each generation . this randomly selected set of training 102 a and evaluation 103 a input vectors are applied to both the newly generated offspring , as well as the best performing members 241 . in this manner , the subsequent best performance selection , at 240 , is provided the measures of performance for each member based on the same set of training and evaluation vectors . by using a random selection of training and evaluation vectors at each generation , only robust members , those that perform well under a variety of circumstances , are likely to survive from generation to generation . after each generation and evaluation of a new current population 210 , the selector 240 selects a set of best performers 241 from each new current population 210 . at some point in time , this process terminates , and the traits 251 of the best performer of the best performers 241 are provided to produce the preferred set of initialization 151 and architecture 152 parameters for production use by the neural network system 100 . because each new generation of offspring is determined based on the traits of the better performing members of the prior generation ( s ), the “ best of the best ” traits 251 are likely to provide an optimum , or near optimum , configuration of the neural network system 100 . the termination of the evolutionary process at 250 may be effected in a variety of ways . generations of new members may be processed until a given time limit is reached , or until the variance among the best performers 241 is minimal , or until a given level of performance is achieved , or any combination of such criteria , as is common in the art . the foregoing merely illustrates the principles of the invention . it will thus be appreciated that those skilled in the art will be able to devise various arrangements which , although not explicitly described or shown herein , embody the principles of the invention and are thus within its spirit and scope . for example , the training parameters 153 may also affect the selection of training input vectors 102 a . for example , the trait 211 that determines the training parameter 153 may be a seed for a random number generator that determines which training vectors 102 a to apply to the neural network 120 for training , or may be used to expressly determine particular vectors 102 a , and so on . in like manner , other techniques for improving the performance of a neural network , or other trainable system , may be integrated with the techniques of this invention as well . the particular structures and functions are presented in the figures for illustration purposes . other system configurations and optimization features will be evident to one of ordinary skill in the art in view of this disclosure , and are included within the scope of the following claims .