diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs
index 33ddf59..6920358 100644
--- a/tig-algorithms/src/knapsack/mod.rs
+++ b/tig-algorithms/src/knapsack/mod.rs
@@ -148,7 +148,8 @@
 
 // c003_a075
 
-// c003_a076
+pub mod relative_opt_mid;
+pub use relative_opt_mid as c003_a076;
 
 // c003_a077
 
diff --git a/tig-algorithms/src/knapsack/relative_opt_mid/README.md b/tig-algorithms/src/knapsack/relative_opt_mid/README.md
new file mode 100644
index 0000000..bd1a09f
--- /dev/null
+++ b/tig-algorithms/src/knapsack/relative_opt_mid/README.md
@@ -0,0 +1,23 @@
+# TIG Code Submission
+
+    ## Submission Details
+
+    * **Challenge Name:** knapsack
+    * **Submission Name:** relative_opt_mid
+    * **Copyright:** 2025 syebastian
+    * **Identity of Submitter:** syebastian
+    * **Identity of Creator of Algorithmic Method:** null
+    * **Unique Algorithm Identifier (UAI):** null
+
+    ## License
+
+    The files in this folder are under the following licenses:
+    * TIG Benchmarker Outbound License
+    * TIG Commercial License
+    * TIG Inbound Game License
+    * TIG Innovator Outbound Game License
+    * TIG Open Data License
+    * TIG THV Game License
+
+    Copies of the licenses can be obtained at:  
+    https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses
\ No newline at end of file
diff --git a/tig-algorithms/src/knapsack/relative_opt_mid/mod.rs b/tig-algorithms/src/knapsack/relative_opt_mid/mod.rs
new file mode 100644
index 0000000..d93ecef
--- /dev/null
+++ b/tig-algorithms/src/knapsack/relative_opt_mid/mod.rs
@@ -0,0 +1,352 @@
+use anyhow::{anyhow, Result};
+use serde_json::{Map, Value};
+use tig_challenges::knapsack::*;
+
+
+pub fn solve_challenge(
+    challenge: &Challenge,
+    save_solution: &dyn Fn(&Solution) -> Result<()>,
+    hyperparameters: &Option<Map<String, Value>>,
+) -> Result<()> {
+    Err(anyhow!("This algorithm is no longer compatible."))
+}
+
+// Old code that is no longer compatible
+#[cfg(none)]
+mod dead_code {
+    use anyhow::Result;
+    use rand::{rngs::StdRng, Rng, SeedableRng};
+    use tig_challenges::knapsack::*;
+ 
+     fn compute_solution(
+        challenge: &SubInstance,
+        contribution_list: &mut [i32],
+        unselected_items: &mut Vec<usize>,
+        rng: &mut StdRng,
+    ) -> Result<Option<(SubSolution, i32)>> {
+        let mut selected_items = Vec::new();
+        let mut total_weight = 0;
+        let mut total_value = 0;
+
+        let inv_weights : Vec<f32> = challenge.weights.iter().map(|&w| 1.0 / w as f32).collect();
+
+        const RCL_MAX: usize = 10;
+
+        let probs: Vec<f32> = (0..RCL_MAX)
+            .map(|rank| 1.0 / ((rank + 1) as f32).exp())
+            .collect();
+
+        let mut acc_probs: Vec<f32> = Vec::with_capacity(RCL_MAX);
+        let mut sum = 0.0;
+        for &prob in &probs {
+            sum += prob;
+            acc_probs.push(sum);
+        }
+        let total_prob_max = sum;
+        let max_item_weight = challenge.weights.iter().max().unwrap();
+
+        let mut item_densities: Vec<(usize, f32)> = unselected_items
+            .iter()
+            .map(|&idx| {
+                let ratio = contribution_list[idx] as f32 * inv_weights[idx];
+                (idx, ratio)
+            })
+            .collect();
+
+        let list_size = 2;
+        let mut top_ranks = vec![0; list_size];
+        let mut top_densities = vec![f32::MIN; list_size];
+
+        while !item_densities.is_empty() {
+            let num_candidates = item_densities.len();
+            if num_candidates < 2 {
+                break;
+            }
+
+            let actual_rcl_size = num_candidates.min(RCL_MAX);
+            let total_prob = if actual_rcl_size == RCL_MAX {
+                total_prob_max
+            } else {
+                acc_probs[actual_rcl_size - 1]
+            };
+
+            let random_threshold = rng.gen_range(0.0..total_prob);
+            let mut selected_rank = match acc_probs[..actual_rcl_size].binary_search_by(|prob| {
+                prob.partial_cmp(&random_threshold).unwrap()
+            }) { Ok(i) | Err(i) => i };
+            if selected_rank >= actual_rcl_size {
+                selected_rank = actual_rcl_size - 1;
+            }
+            let mut selected_item = 0;
+            if selected_rank < list_size && !selected_items.is_empty() {
+                selected_rank = top_ranks[selected_rank];
+                selected_item = item_densities[selected_rank].0;
+            } else {
+                item_densities.select_nth_unstable_by(selected_rank, |a, b| {
+                    b.1.partial_cmp(&a.1).unwrap()
+                });
+                selected_item = item_densities[selected_rank].0;
+            }
+
+            selected_items.push(selected_item);
+            total_weight += challenge.weights[selected_item];
+            total_value += contribution_list[selected_item];
+
+            if total_weight + max_item_weight > challenge.max_weight {
+                item_densities.retain(|(idx, _)| {
+                    total_weight + challenge.weights[*idx] <= challenge.max_weight && *idx != selected_item
+                });
+            } else {
+                item_densities.swap_remove(selected_rank);
+            }
+
+            unsafe {
+                for x in 0..challenge.difficulty.num_items {
+                    *contribution_list.get_unchecked_mut(x) +=
+                        *challenge.interaction_values.get_unchecked(selected_item).get_unchecked(x);
+                }
+        
+                let mut first_density = f32::MIN;
+                let mut first_rank = 0;
+                let mut second_density = f32::MIN;
+                let mut second_rank = 0;
+
+                for (i, density) in item_densities.iter_mut().enumerate() {
+                    let interaction = unsafe {
+                        *challenge.interaction_values.get_unchecked(selected_item).get_unchecked(density.0)
+                    };
+                    density.1 += interaction as f32 * inv_weights[density.0];
+                    let current_density = density.1;
+
+                    if current_density > first_density {
+                        second_density = first_density;
+                        second_rank = first_rank;
+                        first_density = current_density;
+                        first_rank = i;
+                    } else if current_density > second_density {
+                        second_density = current_density;
+                        second_rank = i;
+                    }
+                }
+
+                top_ranks[0] = first_rank;
+                top_ranks[1] = second_rank;
+                top_densities[0] = first_density;
+                top_densities[1] = second_density;
+            }
+        }
+        unselected_items.clear();
+        unselected_items.extend(0..challenge.difficulty.num_items);
+
+        let mut sorted_selected = selected_items.clone();
+        sorted_selected.sort_unstable_by(|a, b| b.cmp(a));
+
+        for &selected in &sorted_selected {
+            unselected_items.swap_remove(selected);
+        }
+    
+        unselected_items.sort_unstable_by_key(|&idx| challenge.weights[idx]);
+
+        let local_search_iterations = 150;
+        let mut feasible_adds = Vec::new();
+        let mut feasible_swaps = Vec::new();
+        for _ in 0..local_search_iterations {
+            let mut improved = false;
+ 
+            if total_weight < challenge.max_weight {
+                for (i, &cand) in unselected_items.iter().enumerate() {
+                    let new_w = total_weight + challenge.weights[cand];
+                    let new_val = total_value + contribution_list[cand];
+                    if new_w > challenge.max_weight {
+                        break;
+                    }
+
+                    if new_val >= total_value {
+                        feasible_adds.push(i);
+                    }
+                }
+                if !feasible_adds.is_empty() {
+                    let pick = rng.gen_range(0..feasible_adds.len());
+                    let add_idx = feasible_adds[pick];
+                    let new_item = unselected_items[add_idx];
+    
+                    unselected_items.remove(add_idx);
+                    selected_items.push(new_item);
+    
+                    total_weight += challenge.weights[new_item];
+                    total_value += contribution_list[new_item];
+                    improved = true;
+    
+                    unsafe {
+                        for x in 0..challenge.difficulty.num_items {
+                            *contribution_list.get_unchecked_mut(x) += 
+                                *challenge.interaction_values.get_unchecked(x).get_unchecked(new_item);
+                        }
+                    }
+                }
+                feasible_adds.clear();
+            }
+
+            let free_capacity = challenge.max_weight as i32 - total_weight as i32;
+            for (j, &rem_item) in selected_items.iter().enumerate() {
+                let rem_w = challenge.weights[rem_item] as i32;
+
+                for (i, &cand_item) in unselected_items.iter().enumerate() {
+                    let cand_w = challenge.weights[cand_item] as i32;
+                    if rem_w + free_capacity < cand_w {
+                        break;
+                    }
+                
+                    let val_diff = contribution_list[cand_item]
+                        - contribution_list[rem_item]
+                        - challenge.interaction_values[cand_item][rem_item];
+                    if val_diff >= 0 {
+                        feasible_swaps.push((i, j));
+                    }
+                }
+            }
+
+            if !feasible_swaps.is_empty() {
+                let pick = rng.gen_range(0..feasible_swaps.len());
+                let (unsel_idx, sel_idx) = feasible_swaps[pick];
+                let new_item = unselected_items[unsel_idx];
+                let remove_item = selected_items[sel_idx];
+ 
+                selected_items.swap_remove(sel_idx);
+                selected_items.push(new_item);
+
+
+                let new_item_weight =  challenge.weights[new_item];
+                let remove_item_weight = challenge.weights[remove_item];
+
+                let current_pos = unsel_idx;
+                let mut target_pos = current_pos;
+                if new_item_weight != remove_item_weight {
+                    target_pos = unselected_items
+                    .binary_search_by(|&probe| challenge.weights[probe].cmp(&remove_item_weight))
+                    .unwrap_or_else(|e| e);
+                }
+                if current_pos != target_pos {
+                    unsafe {
+                        let ptr = unselected_items.as_mut_ptr();
+                        if target_pos < current_pos {
+                            std::ptr::copy(
+                                ptr.add(target_pos),
+                                ptr.add(target_pos + 1),
+                                current_pos - target_pos
+                            );
+                        } else {
+                            target_pos = target_pos - 1;
+                            std::ptr::copy(
+                                ptr.add(current_pos + 1),
+                                ptr.add(current_pos),
+                                target_pos - current_pos
+                            );
+                        }
+                    }
+                }
+                unselected_items[target_pos] = remove_item;
+ 
+ 
+                total_value += contribution_list[new_item]
+                    - contribution_list[remove_item]
+                    - challenge.interaction_values[new_item][remove_item];
+                total_weight = total_weight + challenge.weights[new_item] - challenge.weights[remove_item];
+                improved = true;
+ 
+                unsafe {
+                    for x in 0..challenge.difficulty.num_items {
+                        *contribution_list.get_unchecked_mut(x) += 
+                            *challenge.interaction_values.get_unchecked(x).get_unchecked(new_item) -
+                            *challenge.interaction_values.get_unchecked(x).get_unchecked(remove_item);
+                    }
+                }
+            }
+            feasible_swaps.clear();
+ 
+            if !improved {
+                break;
+            }
+        }
+ 
+        if selected_items.is_empty() {
+            Ok(None)
+        } else {
+            Ok(Some((SubSolution { items: selected_items }, total_value)))
+        }
+     }
+
+ 
+    pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result<Option<Solution>> {
+        let mut solution = Solution {
+            sub_solutions: Vec::new(),
+        };
+        for sub_instance in &challenge.sub_instances {
+            match solve_sub_instance(sub_instance)? {
+                Some(sub_solution) => solution.sub_solutions.push(sub_solution),
+                None => return Ok(None),
+            }
+        }
+        Ok(Some(solution))
+    }
+
+    pub fn solve_sub_instance(challenge: &SubInstance) -> Result<Option<SubSolution>> {
+        let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(
+            challenge.seed[..8].try_into().unwrap(),
+        ));
+ 
+        let mut best_solution: Option<SubSolution> = None;
+        let mut best_value = 0;
+ 
+        for _outer_iter in 0..200 {
+            let mut unselected_items: Vec<usize> = (0..challenge.difficulty.num_items).collect();
+            let mut contribution_list = challenge
+                .values
+                .iter()
+                .map(|&v| v as i32)
+                .collect::<Vec<i32>>();
+ 
+            let sol_result =
+                compute_solution(challenge, &mut contribution_list, &mut unselected_items, &mut rng)?;
+ 
+            let (solution, value) = match sol_result {
+                Some(x) => x,
+                None => continue,
+            };
+ 
+            if value > best_value {
+                best_value = value;
+                best_solution = Some(SubSolution { items: solution.items.clone() });
+            }
+ 
+            let threshold = lookup_threshold(challenge.difficulty.num_items);
+            if (challenge.baseline_value as f32) * (1.0 - threshold * 0.01) >= best_value as f32 {
+                return Ok(None);
+            }
+            else if challenge.baseline_value <= best_value as u32 {
+                return Ok(best_solution);
+            }
+        }
+ 
+        Ok(best_solution)
+     }
+ 
+     fn lookup_threshold(num_items: usize) -> f32 {
+        let points = vec![
+            (100, 1.071), (105, 1.015), (110, 0.973), (120, 0.882),
+            (125, 0.791), (130, 0.770), (135, 0.760), (140, 0.749),
+            (145, 0.700), (150, 0.616), (155, 0.574), (160, 0.532),
+            (165, 0.511), (170, 0.494), (175, 0.485), (180, 0.476),
+            (190, 0.448), (195, 0.434), (200, 0.427), (205, 0.420),
+            (210, 0.420), (215, 0.385), (220, 0.350), (225, 0.347),
+            (230, 0.343), (235, 0.343), (240, 0.338), (245, 0.334),
+            (250, 0.329)
+        ];
+ 
+        points.iter()
+            .filter(|&&(x, _)| x <= num_items)
+            .max_by_key(|&&(x, _)| x)
+            .unwrap()
+            .1
+     }
+}
\ No newline at end of file