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ECJ, A Java-based Evolutionary Computation Research System. ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. ECJ is developed at George Mason University's ECLab Evolutionary Computation Laboratory. The software has nothing to do with its initials' namesake, Evolutionary Computation Journal. ECJ's sister project is MASON, a multi-agent simulation system which dovetails with ECJ nicely.

This subproject is an extension of the Struts tag library. Each JSP custom tag in this library is a subclass of an associated tag in the Struts tag library. One difference is that this tag library does not use "rtexprvalues", it uses the expression evaluation engine in the Jakarta Taglibs implementation of the JSP Standard Tag Library (version 1.0) to evaluate attribute values. In addition, some of the Struts tags were not ported to this library, as it was determined that their functionality was entirely supplied by the JSTL. These particular Struts tags, and the reason for their non-porting will be described in the documentation for this library. In order to fully understand the correct utilization of this library, you must understand the use and operation of the Struts tag library, and the use and operation of the JavaServer Pages Standard Tag Library (hereafter called the "JSTL"), along with the expression language (sometimes called the "EL") used for evaluating attribute values.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.