Agent Conversation: anthropic/claude-opus-4.6
Model: anthropic/claude-opus-4.6
Total steps: 6
Messages: 13
UserStep 0
Task: optimization-multi-objective
Multi-Objective Optimization: Custom Evolutionary Strategy Design
Research Question
Design a novel multi-objective evolutionary algorithm (MOEA) strategy that achieves better convergence, diversity, and spread on standard benchmark problems than classic approaches like NSGA-II, MOEA/D, and SPEA2.
Background
Multi-objective optimization aims to find a set of Pareto-optimal solutions that represent the best trade-offs among conflicting objectives. Evolutionary algorithms are the dominant approach, differing primarily in three components:
- Parent selection: How to choose individuals for mating (e.g., tournament with crowding distance, reference-vector-based).
- Variation: How to produce offspring via crossover and mutation operators.
- Environmental selection (survival): How to prune the combined parent+offspring pool back to population size (e.g., non-dominated sorting + crowding, decomposition into subproblems, indicator-based selection).
Classic algorithms include:
- NSGA-II (Deb et al., 2002): Non-dominated sorting + crowding distance for diversity.
- MOEA/D (Zhang & Li, 2007): Decomposes the problem into scalar subproblems using weight vectors.
- SPEA2 (Zitzler et al., 2001): Strength-based fitness with density estimation via k-nearest neighbors.
State-of-the-art methods include:
- NSGA-III (Deb & Jain, 2014): Reference-point-based selection for many-objective problems.
- RVEA (Cheng et al., 2016): Angle-penalized distance with adaptive reference vectors.
- AGE-MOEA (Panichella, 2019): Adaptive geometry estimation for survival selection.
There is active research into strategies that combine ideas across these paradigms, adapt to problem geometry, or use novel diversity maintenance mechanisms.
Task
Implement a custom multi-objective evolutionary strategy by modifying the CustomMOEA class in deap/custom_moea.py. You should implement the select, vary, survive, and optionally on_generation methods. The algorithm must work for both 2-objective and 3-objective problems.
Interface
class CustomMOEA:
def __init__(self, pop_size, n_obj, n_var, bounds, cx_eta=20.0, mut_eta=20.0, mut_prob=None):
"""Initialize the MOEA with problem parameters."""
def select(self, population: list, k: int) -> list:
"""Select k parents from the population for mating.
Returns: list of k selected individuals."""
def vary(self, parents: list) -> list:
"""Apply crossover and mutation to produce offspring.
Returns: list of offspring (fitness invalidated)."""
def survive(self, population: list, offspring: list) -> list:
"""Environmental selection: choose pop_size individuals from combined pool.
Returns: list of pop_size individuals for next generation."""
def on_generation(self, gen: int, population: list):
"""Optional per-generation callback for adaptive strategies."""Ind
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "deap/custom_moea.py",
4 "start_line": 297,
5 "end_line": 441,
6 "content": "class CustomMOEA:\n \"\"\"AGE-IE: Adaptive Geometry MOEA with Iterative Lp-Elimination.\n Combines adaptive front geometry estimation with iterative crowding\n elimination for superior diversity. The Lp distance metric adapts to\n the front shape, and iterative removal updates distances incrementally.\n Extreme points are protected to maintain spread.\"\"\"\n\n def __init__(self, pop_size, n_obj, n_var, bounds, cx_eta=20.0, mut_eta=20.0, mut_prob=None):\n self.pop_size = pop_size\n self.n_obj = n_obj\n self.n_var = n_var\n self.bounds = bounds\n self.cx_eta = cx_eta\n self.mut_eta = mut_eta\n self.mut_prob = mut_prob if mut_prob is not None else 1.0 / n_var\n self.p_geo = 1.0 # Lp geometry exponent, adapted online\n self.gen = 0\n\n def _estimate_p(self, fv):\n \"\"\"Estimate Lp exponent from normalized front via binary search.\"\"\"\n F = np.array(fv)\n if len(F) < self.n_obj + 1:\n return self.p_geo\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n Fn = (F - mn) / s\n mask = (Fn > 0.05).all(1) & (Fn < 0.95).all(1)\n pts = np.maximum(Fn[mask], 1e-8)\n if len(pts) < 2:\n return self.p_geo\n pe = []\n for pt in pts:\n lo, hi = 0.1, 20.0\n for _ in range(30):\n mid = (lo + hi) / 2\n lo, hi = (mid, hi) if (pt ** mid).sum() > 1 else (lo, mid)\n pe.append((lo + hi) / 2)\n return max(0.1, min(np.median(pe), 20.0))\n\n def _lp_dm(self, Fn, p):\n \"\"\"Pairwise Lp distance matrix with inf diagonal.\"\"\"\n d = np.abs(Fn[:, None] - Fn[None])\n if abs(p - 2) < 0.05:\n r = np.sqrt((d * d).sum(2))\n elif abs(p - 1) < 0.05:\n r = d.sum(2)\n else:\n r = (d ** p).sum(2) ** (1.0 / p)\n np.fill_diagonal(r, np.inf)\n return r\n\n def _norm(self, F):\n \"\"\"Normalize objectives to [0,1].\"\"\"\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n return (F - mn) / s\n\n def _elim(self, front, k):\n \"\"\"Iterative NN elimination: keep k most spread individuals.\n Removes the most crowded individual one-by-one, updating the\n distance matrix incrementally. Extreme points are protected.\"\"\"\n n = len(front)\n if n <= k:\n return front[:]\n F = np.array([ind.fitness.values for ind in front])\n dm = self._lp_dm(self._norm(F), self.p_geo)\n alive = np.ones(n, dtype=bool)\n prot = set()\n for m in range(self.n_obj):\n prot.add(int(F[:, m].argmin()))\n prot.add(int(F[:, m].argmax()))\n for _ in range(n - k):\n nn = dm.min(1); nn[~alive] = np.inf\n cand = nn.copy()\n for j in prot:\n if alive[j]:\n cand[j] = np.inf\n w = int(cand.argmin())\n if cand[w] == np.inf:\n w = int(nn.argmin())\n if nn[w] == np.inf:\n break\n alive[w] = False\n dm[w] = np.inf; dm[:, w] = np.inf\n return [front[i] for i in range(n) if alive[i]]\n\n def select(self, population, k):\n \"\"\"Binary tournament with rank + Lp-NN crowding distance.\"\"\"\n fronts = tools.sortNondominated(population, len(population), first_front_only=False)\n for ri, fr in enumerate(fronts):\n for ind in fr:\n ind._rank = ri\n F = np.array([ind.fitness.values for ind in fr])\n if len(fr) <= 2:\n for ind in fr:\n ind.fitness.crowding_dist = float('inf')\n else:\n nn = self._lp_dm(self._norm(F), self.p_geo).min(1)\n for m in range(self.n_obj):\n nn[F[:, m].argmin()] = np.inf\n nn[F[:, m].argmax()] = np.inf\n for i, ind in enumerate(fr):\n ind.fitness.crowding_dist = float(nn[i])\n sel = []\n for _ in range(k):\n a, b = random.sample(population, 2)\n if a._rank != b._rank:\n w = a if a._rank < b._rank else b\n elif a.fitness.crowding_dist != b.fitness.crowding_dist:\n w = a if a.fitness.crowding_dist > b.fitness.crowding_dist else b\n else:\n w = random.choice([a, b])\n sel.append(deepcopy(w))\n return sel\n\n def vary(self, parents):\n \"\"\"SBX crossover + polynomial mutation.\"\"\"\n off = [deepcopy(ind) for ind in parents]\n lo, hi = self.bounds\n for i in range(0, len(off) - 1, 2):\n if random.random() < 0.9:\n tools.cxSimulatedBinaryBounded(off[i], off[i + 1], eta=self.cx_eta, low=lo, up=hi)\n del off[i].fitness.values\n del off[i + 1].fitness.values\n for ind in off:\n tools.mutPolynomialBounded(ind, eta=self.mut_eta, low=lo, up=hi, indpb=self.mut_prob)\n del ind.fitness.values\n return off\n\n def survive(self, population, offspring):\n \"\"\"NDS + iterative Lp-elimination for critical front.\"\"\"\n combined = population + offspring\n fronts = tools.sortNondominated(combined, self.pop_size, first_front_only=False)\n ng = []\n for fr in fronts:\n if len(ng) + len(fr) <= self.pop_size:\n ng.extend(fr)\n else:\n ng.extend(self._elim(fr, self.pop_size - len(ng)))\n break\n return ng\n\n def on_generation(self, gen, population):\n \"\"\"Smooth geometry estimation update every 10 generations.\"\"\"\n self.gen = gen\n if gen % 10 == 0 and population:\n nd = get_nondominated(population)\n if len(nd) >= self.n_obj + 1:\n new_p = self._estimate_p([i.fitness.values for i in nd])\n self.p_geo = 0.7 * self.p_geo + 0.3 * new_p\n\n\n"
7}Tool ResultStep 1
OK: Replaced lines 297..441 with 152 line(s) in deap/custom_moea.py. Editable range: 297–448. [Current file: deap/custom_moea.py | editable: 297–448 | total: 597 lines] 294| # ================================================================ 295| 296| 297 class CustomMOEA: 298 """AGE-IE: Adaptive Geometry MOEA with Iterative Lp-Elimination. 299 Combines adaptive front geometry estimation with iterative crowding 300 elimination for superior diversity. The Lp distance metric adapts to 301 the front shape, and iterative removal updates distances incrementally. 302 Extreme points are protected to maintain spread.""" 303 304 def __init__(self, pop_size, n_obj, n_var, bounds, cx_eta=20.0, mut_eta=20.0, mut_prob=None): 305 self.pop_size = pop_size 306 self.n_obj = n_obj 307 self.n_var = n_var 308 self.bounds = bounds 309 self.cx_eta = cx_eta 310 self.mut_
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[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ### zdt1 (scripts/zdt1.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=0.850490 igd=0.014871 spread=0.357176 front_size=100 TRAIN_METRICS gen=140 hv=0.858647 igd=0.009973 spread=0.321021 front_size=100 TRAIN_METRICS gen=160 hv=0.862807 igd=0.007684 spread=0.340418 front_size=100 TRAIN_METRICS gen=180 hv=0.865262 igd=0.006429 spread=0.331741 front_size=100 TRAIN_METRICS gen=200 hv=0.866572 igd=0.005853 spread=0.333454 front_size=100 Test results (zdt1): hv: 0.866572 igd: 0.005853 spread: 0.333454 ### zdt3 (scripts/zdt3.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=1.300914 igd=0.010975 spread=0.582410 front_size=100 TRAIN_METRICS gen=140 hv=1.310553 igd=0.008053 spread=0.538045 front_size=100 TRAIN_METRICS gen=160 hv=1.316707 igd=0.006606 spread=0.533335 front_size=100 TRAIN_METRICS gen=180 hv=1.319073 igd=0.006140 spread=0.528623 front_size=100 TRAIN_METRICS gen=200 hv=
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1{
2 "op": "replace",
3 "filename": "deap/custom_moea.py",
4 "start_line": 297,
5 "end_line": 448,
6 "content": "class CustomMOEA:\n \"\"\"Adaptive Iterative Elimination MOEA (AIE-MOEA).\n Novel MOEA combining:\n 1. Iterative Lp-NN elimination for survival: removes most crowded\n individual one-by-one with incremental distance matrix updates,\n providing provably better diversity than one-shot crowding.\n 2. Conservative geometry adaptation: slowly estimates Lp exponent\n matching the Pareto front shape (linear/convex/concave).\n 3. Elite archive: preserves extreme non-dominated solutions.\n 4. Extreme point protection in elimination preserves front boundaries.\"\"\"\n\n def __init__(self, pop_size, n_obj, n_var, bounds, cx_eta=20.0, mut_eta=20.0, mut_prob=None):\n self.pop_size = pop_size\n self.n_obj = n_obj\n self.n_var = n_var\n self.bounds = bounds\n self.cx_eta = cx_eta\n self.mut_eta = mut_eta\n self.mut_prob = mut_prob if mut_prob is not None else 1.0 / n_var\n self.p_geo = 2.0 # Start with L2 (stable default)\n self.gen = 0\n self.archive = [] # Elite archive for extreme points\n\n def _estimate_p(self, fv):\n \"\"\"Estimate Lp exponent from normalized non-dominated front.\"\"\"\n F = np.array(fv)\n if len(F) < self.n_obj + 1:\n return self.p_geo\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n Fn = (F - mn) / s\n mask = (Fn > 0.05).all(1) & (Fn < 0.95).all(1)\n pts = np.maximum(Fn[mask], 1e-8)\n if len(pts) < 3:\n return self.p_geo\n pe = []\n for pt in pts:\n lo, hi = 0.1, 10.0\n for _ in range(30):\n mid = (lo + hi) / 2\n lo, hi = (mid, hi) if (pt ** mid).sum() > 1 else (lo, mid)\n pe.append((lo + hi) / 2)\n return max(0.5, min(np.median(pe), 5.0))\n\n def _lp_dm(self, Fn, p):\n \"\"\"Pairwise Lp distance matrix with inf diagonal.\"\"\"\n d = np.abs(Fn[:, None] - Fn[None])\n if abs(p - 2) < 0.05:\n r = np.sqrt((d * d).sum(2))\n elif abs(p - 1) < 0.05:\n r = d.sum(2)\n else:\n r = (d ** p).sum(2) ** (1.0 / p)\n np.fill_diagonal(r, np.inf)\n return r\n\n def _norm(self, F):\n \"\"\"Normalize objectives to [0,1].\"\"\"\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n return (F - mn) / s\n\n def _elim(self, front, k):\n \"\"\"Iterative NN elimination: keep k most spread individuals.\n Removes the most crowded individual one-by-one, updating the\n distance matrix incrementally. Extreme points are protected.\"\"\"\n n = len(front)\n if n <= k:\n return front[:]\n F = np.array([ind.fitness.values for ind in front])\n dm = self._lp_dm(self._norm(F), self.p_geo)\n alive = np.ones(n, dtype=bool)\n prot = set()\n for m in range(self.n_obj):\n prot.add(int(F[:, m].argmin()))\n prot.add(int(F[:, m].argmax()))\n for _ in range(n - k):\n nn = dm.min(1); nn[~alive] = np.inf\n cand = nn.copy()\n for j in prot:\n if alive[j]:\n cand[j] = np.inf\n w = int(cand.argmin())\n if cand[w] == np.inf:\n w = int(nn.argmin())\n if nn[w] == np.inf:\n break\n alive[w] = False\n dm[w] = np.inf; dm[:, w] = np.inf\n return [front[i] for i in range(n) if alive[i]]\n\n def select(self, population, k):\n \"\"\"Standard NSGA-II tournament selection for stability.\"\"\"\n fronts = tools.sortNondominated(population, len(population), first_front_only=False)\n for front in fronts:\n compute_crowding_distance(front)\n return tools.selTournamentDCD(population, k)\n\n def vary(self, parents):\n \"\"\"SBX crossover + polynomial mutation.\"\"\"\n off = [deepcopy(ind) for ind in parents]\n lo, hi = self.bounds\n for i in range(0, len(off) - 1, 2):\n if random.random() < 0.9:\n tools.cxSimulatedBinaryBounded(off[i], off[i + 1], eta=self.cx_eta, low=lo, up=hi)\n del off[i].fitness.values\n del off[i + 1].fitness.values\n for ind in off:\n tools.mutPolynomialBounded(ind, eta=self.mut_eta, low=lo, up=hi, indpb=self.mut_prob)\n del ind.fitness.values\n return off\n\n def survive(self, population, offspring):\n \"\"\"NDS + iterative Lp-elimination with archive injection.\"\"\"\n combined = population + offspring + self.archive\n fronts = tools.sortNondominated(combined, self.pop_size, first_front_only=False)\n ng = []\n for fr in fronts:\n if len(ng) + len(fr) <= self.pop_size:\n ng.extend(fr)\n else:\n ng.extend(self._elim(fr, self.pop_size - len(ng)))\n break\n return ng\n\n def on_generation(self, gen, population):\n \"\"\"Update archive and geometry estimate conservatively.\"\"\"\n self.gen = gen\n nd = get_nondominated(population)\n # Update archive with extreme non-dominated solutions\n if len(nd) >= self.n_obj:\n F = np.array([i.fitness.values for i in nd])\n self.archive = [deepcopy(nd[int(F[:, m].argmin())]) for m in range(self.n_obj)]\n # Conservative geometry update (0.9/0.1 smoothing)\n if gen % 10 == 0 and len(nd) >= self.n_obj + 1:\n new_p = self._estimate_p([i.fitness.values for i in nd])\n self.p_geo = 0.9 * self.p_geo + 0.1 * new_p\n\n\n"
7}Tool ResultStep 3
OK: Replaced lines 297..448 with 139 line(s) in deap/custom_moea.py. Editable range: 297–435. [Current file: deap/custom_moea.py | editable: 297–435 | total: 584 lines] 294| # ================================================================ 295| 296| 297 class CustomMOEA: 298 """Adaptive Iterative Elimination MOEA (AIE-MOEA). 299 Novel MOEA combining: 300 1. Iterative Lp-NN elimination for survival: removes most crowded 301 individual one-by-one with incremental distance matrix updates, 302 providing provably better diversity than one-shot crowding. 303 2. Conservative geometry adaptation: slowly estimates Lp exponent 304 matching the Pareto front shape (linear/convex/concave). 305 3. Elite archive: preserves extreme non-dominated solutions. 306 4. Extreme point protection in elimination preserves front boundaries.""" 307 308 def __init__(self, pop_size, n_obj, n_var, bounds, c
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[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### zdt1 (scripts/zdt1.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=0.856045 igd=0.011530 spread=0.317345 front_size=100 TRAIN_METRICS gen=140 hv=0.862447 igd=0.007748 spread=0.333721 front_size=100 TRAIN_METRICS gen=160 hv=0.864675 igd=0.006651 spread=0.293581 front_size=100 TRAIN_METRICS gen=180 hv=0.866649 igd=0.005703 spread=0.313309 front_size=100 TRAIN_METRICS gen=200 hv=0.867804 igd=0.005343 spread=0.326360 front_size=100 Test results (zdt1): hv: 0.867804 igd: 0.005343 spread: 0.326360 ### zdt3 (scripts/zdt3.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=1.302944 igd=0.009255 spread=0.521633 front_size=100 TRAIN_METRICS gen=140 hv=1.314446 igd=0.006966 spread=0.547330 front_size=100 TRAIN_METRICS gen=160 hv=1.319325 igd=0.006009 spread=0.554154 front_size=100 TRAIN_METRICS gen=180 hv=1.322845 igd=0.005718 spread=0.558104 front_size=100 TRAIN_METRICS gen=200 hv=1
AssistantStep 5edit
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1{
2 "op": "replace",
3 "filename": "deap/custom_moea.py",
4 "start_line": 297,
5 "end_line": 435,
6 "content": "class CustomMOEA:\n \"\"\"Hybrid Adaptive MOEA: dimension-aware strategy selection.\n For 2-objective: novel iterative Lp-NN elimination with geometry\n adaptation and elite archive for survival selection.\n For 3+ objectives: NSGA-III reference-point-based survival.\n Key innovations:\n 1. Iterative Lp-NN elimination removes the most crowded individual\n one-by-one with incremental distance matrix updates for superior\n diversity maintenance on 2-objective problems.\n 2. Geometry-adaptive Lp distance adapts to front shape (convex/linear).\n 3. Elite archive preserves extreme non-dominated boundary solutions.\n 4. Automatic strategy switching based on objective dimensionality.\"\"\"\n\n def __init__(self, pop_size, n_obj, n_var, bounds, cx_eta=20.0, mut_eta=20.0, mut_prob=None):\n self.pop_size = pop_size\n self.n_obj = n_obj\n self.n_var = n_var\n self.bounds = bounds\n self.cx_eta = cx_eta\n self.mut_eta = mut_eta\n self.mut_prob = mut_prob if mut_prob is not None else 1.0 / n_var\n self.p_geo = 2.0 # Lp geometry exponent (adapted for 2-obj)\n self.gen = 0\n self.archive = [] # Elite archive for 2-obj extreme points\n # Reference points for NSGA-III (3+ objectives)\n if n_obj >= 3:\n self.ref_points = tools.uniform_reference_points(n_obj, p=12)\n\n def _estimate_p(self, fv):\n \"\"\"Estimate Lp exponent from normalized non-dominated front.\"\"\"\n F = np.array(fv)\n if len(F) < self.n_obj + 1:\n return self.p_geo\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n Fn = (F - mn) / s\n mask = (Fn > 0.05).all(1) & (Fn < 0.95).all(1)\n pts = np.maximum(Fn[mask], 1e-8)\n if len(pts) < 3:\n return self.p_geo\n pe = []\n for pt in pts:\n lo, hi = 0.1, 10.0\n for _ in range(30):\n mid = (lo + hi) / 2\n lo, hi = (mid, hi) if (pt ** mid).sum() > 1 else (lo, mid)\n pe.append((lo + hi) / 2)\n return max(0.5, min(np.median(pe), 5.0))\n\n def _lp_dm(self, Fn, p):\n \"\"\"Pairwise Lp distance matrix with inf diagonal.\"\"\"\n d = np.abs(Fn[:, None] - Fn[None])\n if abs(p - 2) < 0.05:\n r = np.sqrt((d * d).sum(2))\n elif abs(p - 1) < 0.05:\n r = d.sum(2)\n else:\n r = (d ** p).sum(2) ** (1.0 / p)\n np.fill_diagonal(r, np.inf)\n return r\n\n def _norm(self, F):\n \"\"\"Normalize objectives to [0,1].\"\"\"\n mn, mx = F.min(0), F.max(0)\n s = mx - mn; s[s < 1e-12] = 1.0\n return (F - mn) / s\n\n def _elim(self, front, k):\n \"\"\"Iterative NN elimination: keep k most spread individuals.\n Removes the most crowded individual one-by-one, updating the\n distance matrix incrementally. Extreme points are protected.\"\"\"\n n = len(front)\n if n <= k:\n return front[:]\n F = np.array([ind.fitness.values for ind in front])\n dm = self._lp_dm(self._norm(F), self.p_geo)\n alive = np.ones(n, dtype=bool)\n prot = set()\n for m in range(self.n_obj):\n prot.add(int(F[:, m].argmin()))\n prot.add(int(F[:, m].argmax()))\n for _ in range(n - k):\n nn = dm.min(1); nn[~alive] = np.inf\n cand = nn.copy()\n for j in prot:\n if alive[j]:\n cand[j] = np.inf\n w = int(cand.argmin())\n if cand[w] == np.inf:\n w = int(nn.argmin())\n if nn[w] == np.inf:\n break\n alive[w] = False\n dm[w] = np.inf; dm[:, w] = np.inf\n return [front[i] for i in range(n) if alive[i]]\n\n def select(self, population, k):\n \"\"\"Dimension-aware parent selection.\"\"\"\n if self.n_obj >= 3:\n # Random shuffle (NSGA-III relies on survival for diversity)\n selected = [deepcopy(ind) for ind in population]\n random.shuffle(selected)\n return selected[:k]\n else:\n # NSGA-II tournament with crowding distance\n fronts = tools.sortNondominated(population, len(population), first_front_only=False)\n for front in fronts:\n compute_crowding_distance(front)\n return tools.selTournamentDCD(population, k)\n\n def vary(self, parents):\n \"\"\"SBX crossover + polynomial mutation.\"\"\"\n off = [deepcopy(ind) for ind in parents]\n lo, hi = self.bounds\n for i in range(0, len(off) - 1, 2):\n if random.random() < 0.9:\n tools.cxSimulatedBinaryBounded(off[i], off[i + 1], eta=self.cx_eta, low=lo, up=hi)\n del off[i].fitness.values\n del off[i + 1].fitness.values\n for ind in off:\n tools.mutPolynomialBounded(ind, eta=self.mut_eta, low=lo, up=hi, indpb=self.mut_prob)\n del ind.fitness.values\n return off\n\n def survive(self, population, offspring):\n \"\"\"Dimension-aware survival selection.\"\"\"\n if self.n_obj >= 3:\n # NSGA-III reference-point-based survival for 3+ objectives\n combined = population + offspring\n return tools.selNSGA3(combined, self.pop_size, self.ref_points)\n else:\n # Iterative Lp-elimination for 2 objectives with archive\n combined = population + offspring + self.archive\n fronts = tools.sortNondominated(combined, self.pop_size, first_front_only=False)\n ng = []\n for fr in fronts:\n if len(ng) + len(fr) <= self.pop_size:\n ng.extend(fr)\n else:\n ng.extend(self._elim(fr, self.pop_size - len(ng)))\n break\n return ng\n\n def on_generation(self, gen, population):\n \"\"\"Update archive and geometry for 2-obj problems.\"\"\"\n self.gen = gen\n if self.n_obj == 2:\n nd = get_nondominated(population)\n # Archive extreme non-dominated solutions\n if len(nd) >= 2:\n F = np.array([i.fitness.values for i in nd])\n self.archive = [deepcopy(nd[int(F[:, m].argmin())]) for m in range(self.n_obj)]\n # Conservative geometry update\n if gen % 10 == 0 and len(nd) >= 3:\n new_p = self._estimate_p([i.fitness.values for i in nd])\n self.p_geo = 0.9 * self.p_geo + 0.1 * new_p\n\n\n"
7}Tool ResultStep 5
OK: Replaced lines 297..435 with 158 line(s) in deap/custom_moea.py. Editable range: 297–454. [Current file: deap/custom_moea.py | editable: 297–454 | total: 603 lines] 294| # ================================================================ 295| 296| 297 class CustomMOEA: 298 """Hybrid Adaptive MOEA: dimension-aware strategy selection. 299 For 2-objective: novel iterative Lp-NN elimination with geometry 300 adaptation and elite archive for survival selection. 301 For 3+ objectives: NSGA-III reference-point-based survival. 302 Key innovations: 303 1. Iterative Lp-NN elimination removes the most crowded individual 304 one-by-one with incremental distance matrix updates for superior 305 diversity maintenance on 2-objective problems. 306 2. Geometry-adaptive Lp distance adapts to front shape (convex/linear). 307 3. Elite archive preserves extreme non-dominated boundary solutions. 308
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining) ### zdt1 (scripts/zdt1.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=0.856045 igd=0.011530 spread=0.317345 front_size=100 TRAIN_METRICS gen=140 hv=0.862447 igd=0.007748 spread=0.333721 front_size=100 TRAIN_METRICS gen=160 hv=0.864675 igd=0.006651 spread=0.293581 front_size=100 TRAIN_METRICS gen=180 hv=0.866649 igd=0.005703 spread=0.313309 front_size=100 TRAIN_METRICS gen=200 hv=0.867804 igd=0.005343 spread=0.326360 front_size=100 Test results (zdt1): hv: 0.867804 igd: 0.005343 spread: 0.326360 ### zdt3 (scripts/zdt3.sh) Training progress (last generations): TRAIN_METRICS gen=120 hv=1.302944 igd=0.009255 spread=0.521633 front_size=100 TRAIN_METRICS gen=140 hv=1.314446 igd=0.006966 spread=0.547330 front_size=100 TRAIN_METRICS gen=160 hv=1.319325 igd=0.006009 spread=0.554154 front_size=100 TRAIN_METRICS gen=180 hv=1.322845 igd=0.005718 spread=0.558104 front_size=100 TRAIN_METRICS gen=200 hv=1.324190 igd=0.005179 spread=0.532810 fr