GA == Genetic Algorithm
Pareto (wave) front == In game theory a Pareto move is one that
gives one or more players a higher payoff without reducing the
payoff to the others. A Pareto optimum is where all the players
are at a point where none can increase their benefit without
reducing that of
Hi,
I have a Multi-objective Genetic Algorithm that i am optimising, the
algorithm outputs the values below. The first objective is maximising the
efficiency of the network by lowering energy depletion(minimising
energy depletion) and the second is maximising the number of nodes to
achieve full
uh... what are those values of?
secondarily, why do some of them seem to show as telephone numbers?
MS
T O wrote:
Hi,
I have a Multi-objective Genetic Algorithm that i am optimising, the
algorithm outputs the values below. The first objective is maximising
the efficiency of the network
So are those values lying on the pareto front found by the GA?
I think we need more info to figure out what you are trying to accomplish! For
a start, what were the design variables of your optimisation?
Chris Brooks
Sent from my ASUS Eee Pad
Michael Schippling sc...@santafe.edu wrote:
OK, this is for my own amusement only...
What the heck are you guys talking about?
By pareto front do you mean the, slowly,
improving set of values discovered by the GA?
I've only heard pareto optimum in game theory,
not GA's, but it seems to fit.
If that list of numbers is meant to represent
Sure, it`s widely used in multiobjective optimisation be it GAs or other
evolutionary algorithms. I read the table of numbers as being two columns for
the 2 objectives, and 11 rows for best GA results so far? Just trying to
understand the optimisation problem, as my background is in
What is a GA?
my background is in computer architecture, network architecture, embedded
systems, applied information theory. So don't have a clue.
On Sun, May 8, 2011 at 12:45 PM, Chris Brooks the_broo...@hotmail.comwrote:
Sure, it`s widely used in multiobjective optimisation be it GAs or
Hi,
They are the digits for the location of the node in the wireless sensor network
field.
They should read as below
0.943164 0.000163251
0.000346453 0.254052
0.583485 0.000419807
0.289261 0.000633636
0.896291 0.000314365
0.100863 0.000900134
0.000730138 0.179723
0.0010259 0.0163291
Hi,
Hi,
They should be the values that show how close the parents are to the optimal
solution. However i may be wrong the code below is the Genetic Algorithm i am
running below. However if you can help me i don't mind which Genetic Algorithm
i use i just want a Multi-Objective Genetic
Hi,
How about if on a 3-d environment would they be optimal?
How about if I wanted to do the below could you help?
What i need is a sensor network simulation for the parameters below
a) network deployment areas are 50×50 and100m×100m
b) initial energy of each sensor node is 1 Joule
c) sensor
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