diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index bb71dd2..b2aeb2c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -22,3 +22,11 @@ jobs:
     - uses: actions/checkout@v4
     - name: build
       run: cd engine && make test
+
+  build-initial-population:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: build
+      run: cd initial-population && make
diff --git a/initial-population/.gitignore b/initial-population/.gitignore
new file mode 100644
index 0000000..c41ca02
--- /dev/null
+++ b/initial-population/.gitignore
@@ -0,0 +1,2 @@
+*.ann
+initial-population
diff --git a/initial-population/Makefile b/initial-population/Makefile
new file mode 100644
index 0000000..a0ea4e4
--- /dev/null
+++ b/initial-population/Makefile
@@ -0,0 +1,21 @@
+CFLAGS = -Wall -Wshadow -O3 -g -march=native
+LDLIBS = -lm
+
+OBJS = genann.o
+
+default: initial-population
+
+%.dep : %.c
+	$(CC) -M $< > $@
+include $(OBJS:.o=.dep)
+
+initial-population: $(OBJS) main.o
+	$(CC) $(CFLAGS) -o $@ $^ $(LDLIBS)
+
+%.o: %.c
+	$(CC) $(CFLAGS) -o $@ -c $<
+
+clean:
+	$(RM) *.o *.ann
+	$(RM) initial-population
+
diff --git a/initial-population/genann.c b/initial-population/genann.c
new file mode 100644
index 0000000..b05fa4f
--- /dev/null
+++ b/initial-population/genann.c
@@ -0,0 +1,405 @@
+/*
+ * GENANN - Minimal C Artificial Neural Network
+ *
+ * Copyright (c) 2015-2018 Lewis Van Winkle
+ *
+ * http://CodePlea.com
+ *
+ * This software is provided 'as-is', without any express or implied
+ * warranty. In no event will the authors be held liable for any damages
+ * arising from the use of this software.
+ *
+ * Permission is granted to anyone to use this software for any purpose,
+ * including commercial applications, and to alter it and redistribute it
+ * freely, subject to the following restrictions:
+ *
+ * 1. The origin of this software must not be misrepresented; you must not
+ *    claim that you wrote the original software. If you use this software
+ *    in a product, an acknowledgement in the product documentation would be
+ *    appreciated but is not required.
+ * 2. Altered source versions must be plainly marked as such, and must not be
+ *    misrepresented as being the original software.
+ * 3. This notice may not be removed or altered from any source distribution.
+ *
+ */
+
+#include "genann.h"
+
+#include <assert.h>
+#include <errno.h>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#ifndef genann_act
+#define genann_act_hidden genann_act_hidden_indirect
+#define genann_act_output genann_act_output_indirect
+#else
+#define genann_act_hidden genann_act
+#define genann_act_output genann_act
+#endif
+
+#define LOOKUP_SIZE 4096
+
+double genann_act_hidden_indirect(const struct genann *ann, double a) {
+    return ann->activation_hidden(ann, a);
+}
+
+double genann_act_output_indirect(const struct genann *ann, double a) {
+    return ann->activation_output(ann, a);
+}
+
+const double sigmoid_dom_min = -15.0;
+const double sigmoid_dom_max = 15.0;
+double interval;
+double lookup[LOOKUP_SIZE];
+
+#ifdef __GNUC__
+#define likely(x)       __builtin_expect(!!(x), 1)
+#define unlikely(x)     __builtin_expect(!!(x), 0)
+#define unused          __attribute__((unused))
+#else
+#define likely(x)       x
+#define unlikely(x)     x
+#define unused
+#pragma warning(disable : 4996) /* For fscanf */
+#endif
+
+
+double genann_act_sigmoid(const genann *ann unused, double a) {
+    if (a < -45.0) return 0;
+    if (a > 45.0) return 1;
+    return 1.0 / (1 + exp(-a));
+}
+
+void genann_init_sigmoid_lookup(const genann *ann) {
+        const double f = (sigmoid_dom_max - sigmoid_dom_min) / LOOKUP_SIZE;
+        int i;
+
+        interval = LOOKUP_SIZE / (sigmoid_dom_max - sigmoid_dom_min);
+        for (i = 0; i < LOOKUP_SIZE; ++i) {
+            lookup[i] = genann_act_sigmoid(ann, sigmoid_dom_min + f * i);
+        }
+}
+
+double genann_act_sigmoid_cached(const genann *ann unused, double a) {
+    assert(!isnan(a));
+
+    if (a < sigmoid_dom_min) return lookup[0];
+    if (a >= sigmoid_dom_max) return lookup[LOOKUP_SIZE - 1];
+
+    size_t j = (size_t)((a-sigmoid_dom_min)*interval+0.5);
+
+    /* Because floating point... */
+    if (unlikely(j >= LOOKUP_SIZE)) return lookup[LOOKUP_SIZE - 1];
+
+    return lookup[j];
+}
+
+double genann_act_linear(const struct genann *ann unused, double a) {
+    return a;
+}
+
+double genann_act_threshold(const struct genann *ann unused, double a) {
+    return a > 0;
+}
+
+genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
+    if (hidden_layers < 0) return 0;
+    if (inputs < 1) return 0;
+    if (outputs < 1) return 0;
+    if (hidden_layers > 0 && hidden < 1) return 0;
+
+
+    const int hidden_weights = hidden_layers ? (inputs+1) * hidden + (hidden_layers-1) * (hidden+1) * hidden : 0;
+    const int output_weights = (hidden_layers ? (hidden+1) : (inputs+1)) * outputs;
+    const int total_weights = (hidden_weights + output_weights);
+
+    const int total_neurons = (inputs + hidden * hidden_layers + outputs);
+
+    /* Allocate extra size for weights, outputs, and deltas. */
+    const int size = sizeof(genann) + sizeof(double) * (total_weights + total_neurons + (total_neurons - inputs));
+    genann *ret = malloc(size);
+    if (!ret) return 0;
+
+    ret->inputs = inputs;
+    ret->hidden_layers = hidden_layers;
+    ret->hidden = hidden;
+    ret->outputs = outputs;
+
+    ret->total_weights = total_weights;
+    ret->total_neurons = total_neurons;
+
+    /* Set pointers. */
+    ret->weight = (double*)((char*)ret + sizeof(genann));
+    ret->output = ret->weight + ret->total_weights;
+    ret->delta = ret->output + ret->total_neurons;
+
+    genann_randomize(ret);
+
+    ret->activation_hidden = genann_act_sigmoid_cached;
+    ret->activation_output = genann_act_sigmoid_cached;
+
+    genann_init_sigmoid_lookup(ret);
+
+    return ret;
+}
+
+
+genann *genann_read(FILE *in) {
+    int inputs, hidden_layers, hidden, outputs;
+    int rc;
+
+    errno = 0;
+    rc = fscanf(in, "%d %d %d %d", &inputs, &hidden_layers, &hidden, &outputs);
+    if (rc < 4 || errno != 0) {
+        perror("fscanf");
+        return NULL;
+    }
+
+    genann *ann = genann_init(inputs, hidden_layers, hidden, outputs);
+
+    int i;
+    for (i = 0; i < ann->total_weights; ++i) {
+        errno = 0;
+        rc = fscanf(in, " %le", ann->weight + i);
+        if (rc < 1 || errno != 0) {
+            perror("fscanf");
+            genann_free(ann);
+
+            return NULL;
+        }
+    }
+
+    return ann;
+}
+
+
+genann *genann_copy(genann const *ann) {
+    const int size = sizeof(genann) + sizeof(double) * (ann->total_weights + ann->total_neurons + (ann->total_neurons - ann->inputs));
+    genann *ret = malloc(size);
+    if (!ret) return 0;
+
+    memcpy(ret, ann, size);
+
+    /* Set pointers. */
+    ret->weight = (double*)((char*)ret + sizeof(genann));
+    ret->output = ret->weight + ret->total_weights;
+    ret->delta = ret->output + ret->total_neurons;
+
+    return ret;
+}
+
+
+void genann_randomize(genann *ann) {
+    int i;
+    for (i = 0; i < ann->total_weights; ++i) {
+        double r = GENANN_RANDOM();
+        /* Sets weights from -0.5 to 0.5. */
+        ann->weight[i] = r - 0.5;
+    }
+}
+
+
+void genann_free(genann *ann) {
+    /* The weight, output, and delta pointers go to the same buffer. */
+    free(ann);
+}
+
+
+double const *genann_run(genann const *ann, double const *inputs) {
+    double const *w = ann->weight;
+    double *o = ann->output + ann->inputs;
+    double const *i = ann->output;
+
+    /* Copy the inputs to the scratch area, where we also store each neuron's
+     * output, for consistency. This way the first layer isn't a special case. */
+    memcpy(ann->output, inputs, sizeof(double) * ann->inputs);
+
+    int h, j, k;
+
+    if (!ann->hidden_layers) {
+        double *ret = o;
+        for (j = 0; j < ann->outputs; ++j) {
+            double sum = *w++ * -1.0;
+            for (k = 0; k < ann->inputs; ++k) {
+                sum += *w++ * i[k];
+            }
+            *o++ = genann_act_output(ann, sum);
+        }
+
+        return ret;
+    }
+
+    /* Figure input layer */
+    for (j = 0; j < ann->hidden; ++j) {
+        double sum = *w++ * -1.0;
+        for (k = 0; k < ann->inputs; ++k) {
+            sum += *w++ * i[k];
+        }
+        *o++ = genann_act_hidden(ann, sum);
+    }
+
+    i += ann->inputs;
+
+    /* Figure hidden layers, if any. */
+    for (h = 1; h < ann->hidden_layers; ++h) {
+        for (j = 0; j < ann->hidden; ++j) {
+            double sum = *w++ * -1.0;
+            for (k = 0; k < ann->hidden; ++k) {
+                sum += *w++ * i[k];
+            }
+            *o++ = genann_act_hidden(ann, sum);
+        }
+
+        i += ann->hidden;
+    }
+
+    double const *ret = o;
+
+    /* Figure output layer. */
+    for (j = 0; j < ann->outputs; ++j) {
+        double sum = *w++ * -1.0;
+        for (k = 0; k < ann->hidden; ++k) {
+            sum += *w++ * i[k];
+        }
+        *o++ = genann_act_output(ann, sum);
+    }
+
+    /* Sanity check that we used all weights and wrote all outputs. */
+    assert(w - ann->weight == ann->total_weights);
+    assert(o - ann->output == ann->total_neurons);
+
+    return ret;
+}
+
+
+void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate) {
+    /* To begin with, we must run the network forward. */
+    genann_run(ann, inputs);
+
+    int h, j, k;
+
+    /* First set the output layer deltas. */
+    {
+        double const *o = ann->output + ann->inputs + ann->hidden * ann->hidden_layers; /* First output. */
+        double *d = ann->delta + ann->hidden * ann->hidden_layers; /* First delta. */
+        double const *t = desired_outputs; /* First desired output. */
+
+
+        /* Set output layer deltas. */
+        if (genann_act_output == genann_act_linear ||
+                ann->activation_output == genann_act_linear) {
+            for (j = 0; j < ann->outputs; ++j) {
+                *d++ = *t++ - *o++;
+            }
+        } else {
+            for (j = 0; j < ann->outputs; ++j) {
+                *d++ = (*t - *o) * *o * (1.0 - *o);
+                ++o; ++t;
+            }
+        }
+    }
+
+
+    /* Set hidden layer deltas, start on last layer and work backwards. */
+    /* Note that loop is skipped in the case of hidden_layers == 0. */
+    for (h = ann->hidden_layers - 1; h >= 0; --h) {
+
+        /* Find first output and delta in this layer. */
+        double const *o = ann->output + ann->inputs + (h * ann->hidden);
+        double *d = ann->delta + (h * ann->hidden);
+
+        /* Find first delta in following layer (which may be hidden or output). */
+        double const * const dd = ann->delta + ((h+1) * ann->hidden);
+
+        /* Find first weight in following layer (which may be hidden or output). */
+        double const * const ww = ann->weight + ((ann->inputs+1) * ann->hidden) + ((ann->hidden+1) * ann->hidden * (h));
+
+        for (j = 0; j < ann->hidden; ++j) {
+
+            double delta = 0;
+
+            for (k = 0; k < (h == ann->hidden_layers-1 ? ann->outputs : ann->hidden); ++k) {
+                const double forward_delta = dd[k];
+                const int windex = k * (ann->hidden + 1) + (j + 1);
+                const double forward_weight = ww[windex];
+                delta += forward_delta * forward_weight;
+            }
+
+            *d = *o * (1.0-*o) * delta;
+            ++d; ++o;
+        }
+    }
+
+
+    /* Train the outputs. */
+    {
+        /* Find first output delta. */
+        double const *d = ann->delta + ann->hidden * ann->hidden_layers; /* First output delta. */
+
+        /* Find first weight to first output delta. */
+        double *w = ann->weight + (ann->hidden_layers
+                ? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * ann->hidden * (ann->hidden_layers-1))
+                : (0));
+
+        /* Find first output in previous layer. */
+        double const * const i = ann->output + (ann->hidden_layers
+                ? (ann->inputs + (ann->hidden) * (ann->hidden_layers-1))
+                : 0);
+
+        /* Set output layer weights. */
+        for (j = 0; j < ann->outputs; ++j) {
+            *w++ += *d * learning_rate * -1.0;
+            for (k = 1; k < (ann->hidden_layers ? ann->hidden : ann->inputs) + 1; ++k) {
+                *w++ += *d * learning_rate * i[k-1];
+            }
+
+            ++d;
+        }
+
+        assert(w - ann->weight == ann->total_weights);
+    }
+
+
+    /* Train the hidden layers. */
+    for (h = ann->hidden_layers - 1; h >= 0; --h) {
+
+        /* Find first delta in this layer. */
+        double const *d = ann->delta + (h * ann->hidden);
+
+        /* Find first input to this layer. */
+        double const *i = ann->output + (h
+                ? (ann->inputs + ann->hidden * (h-1))
+                : 0);
+
+        /* Find first weight to this layer. */
+        double *w = ann->weight + (h
+                ? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * (ann->hidden) * (h-1))
+                : 0);
+
+
+        for (j = 0; j < ann->hidden; ++j) {
+            *w++ += *d * learning_rate * -1.0;
+            for (k = 1; k < (h == 0 ? ann->inputs : ann->hidden) + 1; ++k) {
+                *w++ += *d * learning_rate * i[k-1];
+            }
+            ++d;
+        }
+
+    }
+
+}
+
+
+void genann_write(genann const *ann, FILE *out) {
+    fprintf(out, "%d %d %d %d", ann->inputs, ann->hidden_layers, ann->hidden, ann->outputs);
+
+    int i;
+    for (i = 0; i < ann->total_weights; ++i) {
+        fprintf(out, " %.20e", ann->weight[i]);
+    }
+}
+
+
diff --git a/initial-population/genann.h b/initial-population/genann.h
new file mode 100644
index 0000000..e4b7383
--- /dev/null
+++ b/initial-population/genann.h
@@ -0,0 +1,108 @@
+/*
+ * GENANN - Minimal C Artificial Neural Network
+ *
+ * Copyright (c) 2015-2018 Lewis Van Winkle
+ *
+ * http://CodePlea.com
+ *
+ * This software is provided 'as-is', without any express or implied
+ * warranty. In no event will the authors be held liable for any damages
+ * arising from the use of this software.
+ *
+ * Permission is granted to anyone to use this software for any purpose,
+ * including commercial applications, and to alter it and redistribute it
+ * freely, subject to the following restrictions:
+ *
+ * 1. The origin of this software must not be misrepresented; you must not
+ *    claim that you wrote the original software. If you use this software
+ *    in a product, an acknowledgement in the product documentation would be
+ *    appreciated but is not required.
+ * 2. Altered source versions must be plainly marked as such, and must not be
+ *    misrepresented as being the original software.
+ * 3. This notice may not be removed or altered from any source distribution.
+ *
+ */
+
+
+#ifndef GENANN_H
+#define GENANN_H
+
+#include <stdio.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#ifndef GENANN_RANDOM
+/* We use the following for uniform random numbers between 0 and 1.
+ * If you have a better function, redefine this macro. */
+#define GENANN_RANDOM() (((double)rand())/RAND_MAX)
+#endif
+
+struct genann;
+
+typedef double (*genann_actfun)(const struct genann *ann, double a);
+
+typedef struct genann {
+    /* How many inputs, outputs, and hidden neurons. */
+    int inputs, hidden_layers, hidden, outputs;
+
+    /* Which activation function to use for hidden neurons. Default: gennann_act_sigmoid_cached*/
+    genann_actfun activation_hidden;
+
+    /* Which activation function to use for output. Default: gennann_act_sigmoid_cached*/
+    genann_actfun activation_output;
+
+    /* Total number of weights, and size of weights buffer. */
+    int total_weights;
+
+    /* Total number of neurons + inputs and size of output buffer. */
+    int total_neurons;
+
+    /* All weights (total_weights long). */
+    double *weight;
+
+    /* Stores input array and output of each neuron (total_neurons long). */
+    double *output;
+
+    /* Stores delta of each hidden and output neuron (total_neurons - inputs long). */
+    double *delta;
+
+} genann;
+
+/* Creates and returns a new ann. */
+genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
+
+/* Creates ANN from file saved with genann_write. */
+genann *genann_read(FILE *in);
+
+/* Sets weights randomly. Called by init. */
+void genann_randomize(genann *ann);
+
+/* Returns a new copy of ann. */
+genann *genann_copy(genann const *ann);
+
+/* Frees the memory used by an ann. */
+void genann_free(genann *ann);
+
+/* Runs the feedforward algorithm to calculate the ann's output. */
+double const *genann_run(genann const *ann, double const *inputs);
+
+/* Does a single backprop update. */
+void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
+
+/* Saves the ann. */
+void genann_write(genann const *ann, FILE *out);
+
+void genann_init_sigmoid_lookup(const genann *ann);
+double genann_act_sigmoid(const genann *ann, double a);
+double genann_act_sigmoid_cached(const genann *ann, double a);
+double genann_act_threshold(const genann *ann, double a);
+double genann_act_linear(const genann *ann, double a);
+
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /*GENANN_H*/
diff --git a/initial-population/main.c b/initial-population/main.c
new file mode 100644
index 0000000..85619a2
--- /dev/null
+++ b/initial-population/main.c
@@ -0,0 +1,72 @@
+/*
+
+MIT License
+
+Copyright (c) 2023 Urban Hafner
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+*/
+
+#include <stdlib.h>
+
+#include "genann.h"
+
+int main(int argc, char **argv) {
+  // Do not buffer stdout
+  setbuf(stdout, NULL);
+
+  int population_size, board_size, hidden_layers, hidden;
+
+  if (argc != 5) {
+    fprintf(stderr, "4 arguments required: population_size, board size, no. hidden layers, no. neurons per layer!\n");
+    exit(1);
+  }
+
+  population_size = atoi(argv[1]);
+  board_size = atoi(argv[2]);
+  hidden_layers = atoi(argv[3]);
+  hidden = atoi(argv[4]);
+
+  printf(
+    "population_size = %d, board_size = %d, hidden_layers = %d, hidden = %d\n",
+    population_size,
+    board_size,
+    hidden_layers,
+    hidden
+  );
+
+  char buffer[10];
+  // Pass in the komi
+  int inputs = (board_size * board_size) + 1;
+  // Allow pass move
+  int outputs = (board_size * board_size) + 1;
+
+  for(int i = 1; i <= population_size; i++) {
+    printf("\r%d/%d", i, population_size);
+    sprintf(buffer, "%04d.ann", i);
+
+    FILE *fd = fopen(buffer, "w");
+    genann *ann = genann_init(inputs, hidden_layers, hidden, outputs);
+    genann_write(ann, fd);
+    genann_free(ann);
+    fclose(fd);
+  }
+  printf("\n");
+}