Package ch.idsia.ai.ea

Examples of ch.idsia.ai.ea.ES.nextGeneration()

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        ES es = new ES (task, initial, populationSize);
        System.out.println("Evolving " + initial + " with task " + task);
        final String fileName = "evolved" + (int) (Math.random () * Integer.MAX_VALUE) + ".xml";
        for (int gen = 0; gen < generations; gen++) {
            task.setStartingSeed((int) (Math.random () * Integer.MAX_VALUE));
            es.nextGeneration();
            double bestResult = es.getBestFitnesses()[0];
            System.out.println("Generation " + gen + " best " + bestResult);
            Evolvable bestEvolvable = es.getBests()[0];
            double[] fitnesses = task.evaluate((Agent) bestEvolvable);
            System.out.printf("%.4f  %.4f  %.4f  %.4f  %.4f\n",
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        options.setLevelRandSeed((int) (Math.random () * Integer.MAX_VALUE));
        ES es = new ES (task, initial, populationSize);
        System.out.println("Evolving " + initial + " with task " + task);
        final String fileName = "evolved" + (int) (Math.random () * Integer.MAX_VALUE) + ".xml";
        for (int gen = 0; gen < generations; gen++) {
            es.nextGeneration();
            double bestResult = es.getBestFitnesses()[0];
            System.out.println("Generation " + gen + " best " + bestResult);
            Easy.save (es.getBests()[0], fileName);
        }
        Stats.main(new String[]{fileName, "1"});
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        System.out.println("Evolving " + initial + " with task " + task);
        int difficulty = 0;
        final String fileName = "evolved" + (int) (Math.random () * Integer.MAX_VALUE) + ".xml";
        options.setLevelRandSeed(seed);
        for (int gen = 0; gen < generations; gen++) {
            es.nextGeneration();
            double bestResult = es.getBestFitnesses()[0];
            System.out.println("Generation " + gen + " diff " + difficulty + "  best " + bestResult);
            Easy.save (es.getBests()[0], fileName);
            if (bestResult > 4000) {
                difficulty++;
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            task.setNumberOfSeeds(3);
            task.setStartingSeed(0);
            ES es = new ES (task, initial, populationSize);
            System.out.println("Evolving " + initial + " with task " + task);
            for (int gen = 0; gen < generations; gen++) {
                es.nextGeneration();
                double bestResult = es.getBestFitnesses()[0];
                System.out.println("Generation " + gen + " best " + bestResult);
                options.setVisualization(gen % 5 == 0 || bestResult > 4000);
                options.setMaxFPS(true);
                Agent a = (Agent) es.getBests()[0];
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            task.setStartingSeed(0);
            ES es = new ES (task, initial, populationSize);
            System.out.println("Evolving " + initial + " with task " + task);
            for (int gen = 0; gen < generations; gen++) {
                //task.setStartingSeed((int)(Math.random () * Integer.MAX_VALUE));
                es.nextGeneration();
                double bestResult = es.getBestFitnesses()[0];
                System.out.println("Generation " + gen + " best " + bestResult);
                options.setVisualization(gen % 5 == 0 || bestResult > 4000);
                options.setMaxFPS(true);
                Agent a = (Agent) es.getBests()[0];
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            Task task = new ProgressTask(options);
            ES es = new ES (task, initial, populationSize);

            for (int gen = 0; gen < generations; gen++) {
                es.nextGeneration();
                double bestResult = es.getBestFitnesses()[0];
//                LOGGER.println("Generation " + gen + " best " + bestResult, LOGGER.VERBOSE_MODE.INFO);
                System.out.println("Generation " + gen + " best " + bestResult);
                options.setVisualization(gen % 5 == 0 || bestResult > 4000);
                options.setMaxFPS(true);
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