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Re: ecsuite examples

From:

Christopher Vo <[log in to unmask]>

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ECJ Evolutionary Computation Toolkit <[log in to unmask]>

Date:

Sat, 16 Feb 2008 22:01:18 -0500

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 ```Martin V. wrote: > - Do all of 7 functions have minimum at 0.0? Many of the functions in ECSuite are inverted so that ECJ can perform maximization instead of minimization. You can see how the functions are implemented and look at the fitness of the ideal individuals inside the evaluate() method in ec/app/ecsuite/ECSuite.java. There you can see that with the Rosenbrock, Rastrigrin, Sphere, and Step problem types, an individual's fitness is ideal when the fitness is equal to 0.0f. Noisy quartic, Booth, and Griewangk have no ideal fitness value defined. > - What is the probability of crossover (one-type)? No parameter is > specified in ecsuite.params, like the one for mutation probability > (0.005). VectorCrossoverPipeline (which is used in ecsuite.params) grabs individuals from the two sources and always performs the crossover. Note that there also exists a parameter "pop.subpop.0.species.crossover-prob", however it is only for use with any-point crossover. > - How can I find ideal individual (minimum of the function), do I need > to change some parameters? Now for rastrigin (default function) I find > the best individual have value of about -235 (towards zero is better). > I have run the evolution few times with random seed but with no luck > to get fitnes towards 0. Maybe I need to change mutation probability, > the number of generations, the number of individuals, the size of the > genome...etc.? Rastrigin is a function with a lot of local minima. You will have to do your own experimentation with the various parameters to find what parameters work well. Note that with floating point vectors, you may not be able to get exactly 0.0, but approximately 0.0. Actually, in my experience, if you see "Found Ideal Individual" on one of the ECSuite problems, it is often an indicator that something went wrong... Try increasing the population size (pop.subpop.0.size). Also, just to make sure things are working, you may just want to try the same function with a smaller genome size. For example, try 10 genes instead of 50. You can adjust the genome size via the genome-size parameter. You might also try experimenting with Gaussian mutation instead of reset mutation for this problem. --Chris Vo ```

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