George Mason University has won a 3-year NSF grant specifically to enhance the ECJ evolutionary computation toolkit into a broad-based library useful to the entire metaheuristics community.


We need two things:

	1. We need suggestions for what we should do beyond the list below (which is a summary of roughly what we proposed).  In order to convert ECJ into a general-purpose toolkit that could serve as a central library to the generl metaheuristics community, what is missing from our proposed work below?  What would you like to see?

	2. We'll be building a board of "power users" of ECJ to assess the work and make recommendations here and there.  It'll be infrequent and minimal work on your part, but it's important for us.  I would like you to recommend (directly to me) people to be on that board.  Yes you can recommend yourself.

Here's what we have proposed.  What do you think should also be there?

1. Make ECJ More Robust
	1A. Construct a test harness for ECJ
	1B. Make distribution-based system tests for ECJ
	1C. Make unit tests for ECJ

2. Add Metaheuristics Frameworks to ECJ
	2A. Build an Efficient Single-State optimization package (for hill-climbing simualted annealing, tabu search, etc.)
	2B. Build a Combinatorial Optimization package (for GRASP, AS, ACS, and likely certain more recent ACO algorithms -- suggestions?)
	2C. Abstract and extend the Model Fitting Package beyond CMA-ES (probably to PBIL, UMDA, BIPOP-CMA-ES or AMaLGaM IDEA -- suggestions?)
	2D. Build tools to permit hybrid architectures (ILS, memetic algorithms), building off if our mete-evolution facility. Suggestions of specific algorithms to include?
	2E. Add NEAT

3. Make ECJ Easier to Use
	3A. Eclipse integration (wizards, code skeletons)
	3B. Very significantly revise and update ECJ's GUI 

4. Make ECJ More Useful for Analysis
	4A. Facilities to dump statistics directly to R.
	4B. Integrate standard implementations of statistical analyses, such as T-tests etc.
	4C. Add significant number of vector benchmarks, from BBOB etc.
	4D. Work with representation-specific communities (notably GP, ACO, NEAT) to add often-used benchmark problems.