What is ?
e is an advanced Evolutionary Algorithm (EA) Tool designed to solve very difficult problems. e uses evolutionary processes to discover relationships, functions, and programs from the inherent information in a set ofx “training” data.

Based on user supplied examples of the problem (the training data), e evolves generalizations in the form of a straightforward, procedural program. e goes beyond the simple paradigm of evolutionary algorithms by encapsulating these programs into reusable components, and storing them in a library which represents accumulated learning. This library of past learning is available to solve new problems.

You can tackle your difficult problems with the EA that gets smarter as it learns from experience. e is a mature evolutionary algorithm tool in use to solve industry and Government problems for over 5 years. Potential applications are limited only by your imagination. Apply e to problems in:

Engineering	Economics	Signal Processing
Control	Pattern Recognition	Finance
Distribution	Sensor Fusion	Management
Mathematics	Science	Numerical Analysis

To illustrate how e works, consider the following simple example training set. “X1” is the independent variable, and the “Target” column represents the value of the desired function.

One e run evolved the program shown below to represent the training set above.

e derives the program and any needed coefficients.

In the example above, e took seconds to discover .

Of course, this example is a very simple data set represented by an equally simple function, but e is designed to be used on very large data sets with thousands of records representing unknown and complex non-linear relationships.

The table below gives a sample of available e functions and operators.


e incorporates many advanced, powerful, and unique EA features.
Major Features

	Evolves programs and coefficients.
	Multiple Ecosystems.
	Entities migrate and intermarry among ecosystems along preset paths or make your own arbitrarily complex migration patterns.
	Synthetic Annealing (Hill Climbing) integrated into the evolutionary algorithm.
	The solution length and number of coefficients in the solution are determined at run-time.
	Programs are automatically culled for instructions that don't change the outcome.
	You can provide program coefficients or allow e to create its own.
	Populations can easily be saved and reused.
	Libraries of past solutions are automatically applied to the current problem. Current solutions augment the library of stored learning.
	Build multiple libraries for different problem areas.

Other Features

	Configure e to solve floating point problems or make binary decisions.
	Configure e to evolve stock trader programs that output buy, sell and hold signals.
	Use decode utility to directly apply an e program to new input data.
	User can constrain program size.
	User can constrain coefficients in number and range.
	All reproduction parameters are configurable.
	Reproduction operators include crossover, X, blending, mutation, and annealing.
	Configure e to terminate according to any combination of fitness, number of generations, and elapsed time.
	Rich set of configurable program primitives, including arithmetic, logical, conditional, and indirect operators.
	Configurable number of registers available to an e program.
	Scoring options available.

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