| 1 | # Copyright 2006 by Sean Luke and George Mason University
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| 2 | # Licensed under the Academic Free License version 3.0
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| 3 | # See the file "LICENSE" for more information
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| 4 |
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| 5 | #uncomment the desired multiobjective algorithm
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| 6 | #parent.0 = spea2.params
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| 7 | parent.0 = nsga2.params
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| 8 |
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| 9 | eval.problem = ec.app.moosuite.MooSuite
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| 10 |
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| 11 | #
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| 12 | # optionally your type could be: zdt1, zdt2, zdt3, zdt4, zdt6, sphere
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| 13 | #
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| 14 | # Different problems has different default settings, so we suggest
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| 15 | # using the appropriate params file for each benchmark.
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| 16 |
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| 17 | pop.subpop.0.species = ec.vector.FloatVectorSpecies
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| 18 | pop.subpop.0.species.ind = ec.vector.DoubleVectorIndividual
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| 19 | pop.subpop.0.species.fitness.num-objectives = 2
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| 20 | pop.subpop.0.species.fitness.maximize = false
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| 21 | seed.0 = time
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| 22 |
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| 23 |
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| 24 | # Uncomment this if you'd like to force reevaluation of all archive
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| 25 | # members each generation.
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| 26 | #
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| 27 | # breed.reevaluate-elites.0 = true
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| 28 |
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| 29 |
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| 30 | # The multiobjective optimization routines here can use any crossover
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| 31 | # and mutation pipeline you like, but the literature tends to stick with
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| 32 | # SBX for crossover and Polynomial Mutation. And with good reason: our
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| 33 | # tests indicate that Polynomial Mutation is much better than Gaussian of
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| 34 | # any setting for these problems. And for SPEA2 at least, SBX seems to
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| 35 | # outperform most other crossover operators. Note that we're using the
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| 36 | # "bounded" Polynomial Mutation variant, which seems to do a bit better.
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| 37 |
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| 38 | pop.subpop.0.species.crossover-type = sbx
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| 39 | pop.subpop.0.species.crossover-distribution-index = 20
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| 40 |
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| 41 | pop.subpop.0.species.mutation-type = polynomial
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| 42 | pop.subpop.0.species.mutation-distribution-index = 20
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| 43 | pop.subpop.0.species.mutation-bounded = true
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| 44 |
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