<commit_before>Created; 15-03-2014 23:43
Edited;  15-03-2014 23:57

TO-DO List;

- Determine if we will use the current code (advantage; simple to understand, disadvantage; simple implementation)
- If we do continue; 
	* Decide what we will compare (old vs. new with our improvements, or also against more?)
	* If we decide to also compare with greedy optimisation we need a greedy-algorithm
	* Determine what to add as improvement(s)/comparison:
		o Remove the "randomness" by replenishing the cell array in a more "smarter" way 
		o Allow for different possible affinity-measures between cells (now only euclidian distance, but also manhattan or cosine/sine)
		o Different mutation process. Now; proportional to fitness. Comparison; what if based on static weight?
		o Global mutation process. Now; none. Improvement; mutate all cells such that there is a high probability that their affinity gets stronger/higher
		o [complex] Allow for self-adapting parameters for the thresholds
		o [complex] Implement the capability of the algorithm to maintain several distinct networks based on grouped affinities between cells. Requires first the identification of sub-networks inside the initial netwerk, followed by a split of the population into two new ones based on these sub-networks.
		o more?
	* Determine evaluation function to use, or multiple
	* Test the chosen evaluation function(s)
	* Create an additional experimental class to run our experiments
	* Implement our improvements/comparisons
	* Create and additional evaluation class to evaluate the raw data into statisticall data
- If we do not continue; 
	* What code will we use? (there is a complex and vast AINet toolbox for matlab I believe)
	* Or will we implement it ourselves? And why?


test
	<commit_msg>Revert "test commit"<commit_after>Created; 15-03-2014 23:43
Edited;  15-03-2014 23:57

TO-DO List;

- Determine if we will use the current code (advantage; simple to understand, disadvantage; simple implementation)
- If we do continue; 
	* Decide what we will compare (old vs. new with our improvements, or also against more?)
	* If we decide to also compare with greedy optimisation we need a greedy-algorithm
	* Determine what to add as improvement(s)/comparison:
		o Remove the "randomness" by replenishing the cell array in a more "smarter" way 
		o Allow for different possible affinity-measures between cells (now only euclidian distance, but also manhattan or cosine/sine)
		o Different mutation process. Now; proportional to fitness. Comparison; what if based on static weight?
		o Global mutation process. Now; none. Improvement; mutate all cells such that there is a high probability that their affinity gets stronger/higher
		o [complex] Allow for self-adapting parameters for the thresholds
		o [complex] Implement the capability of the algorithm to maintain several distinct networks based on grouped affinities between cells. Requires first the identification of sub-networks inside the initial netwerk, followed by a split of the population into two new ones based on these sub-networks.
		o more?
	* Determine evaluation function to use, or multiple
	* Test the chosen evaluation function(s)
	* Create an additional experimental class to run our experiments
	* Implement our improvements/comparisons
	* Create and additional evaluation class to evaluate the raw data into statisticall data
- If we do not continue; 
	* What code will we use? (there is a complex and vast AINet toolbox for matlab I believe)
	* Or will we implement it ourselves? And why?
	
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