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GeneticAlgorithm
0.5 (beta)
A python framework for rapid GA prototyping
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Go to the source code of this file.
Classes | |
| class | KnapsackDemo.Knapsack |
| An evaluation operator that computes the relaxed version of the objective function used to solve a knapsack instance. More... | |
Namespaces | |
| namespace | KnapsackDemo |
Variables | |
| int | KnapsackDemo.nObjects = 12 |
| list | KnapsackDemo.objectVolumes = [random.randrange(1, 20) for i in xrange(nObjects)] |
| list | KnapsackDemo.objectCosts = [random.randrange(10, 20) for i in xrange(nObjects)] |
| tuple | KnapsackDemo.maxVolume = reduce(lambda x, y: x+y, objectVolumes) |
| int | KnapsackDemo.volumeLambda = 10 |
| KnapsackDemo.maximize = True | |
| int | KnapsackDemo.nGenerations = 5 |
| int | KnapsackDemo.popSize = 10 |
| tuple | KnapsackDemo.genSize = int(math.floor( popSize/10 )) |
| tuple | KnapsackDemo.ch = Core.Genotype(segments=[GenotypeLibrary.BinaryChromosomeSegment(nBits=1) for i in range(nObjects)]) |
| tuple | KnapsackDemo.p = Core.Population(schema=ch, popSize=popSize, genSize=genSize, maximize=maximize, mutation_probability=0.01) |
| tuple | KnapsackDemo.ga |