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 # ============================================================================
 # DTLZ2 Multi-Objective Optimization with Combined Objectives
 # ============================================================================
 # This example shows:
 #   1. Combined objective: single computation produces multiple metrics (f1, f2)
 #   2. Scheduler cascade: global → workflow → branch → stage
 #   3. Clean separation: problem definition → workflow execution
 # ============================================================================
 
 # SECTION 1: PROBLEM DEFINITION
 # What to optimize (objectives) and design space
 # ============================================================================
 problem:
   name: "DTLZ2 Multi-Objective Optimization"
   type: "toy"
   description: "Two-objective DTLZ2 test problem with combined evaluation"
 
   # Design space parameters
   design_space:
     path: "examples/complete/design.params"
 
   # ========================================================================
   # OBJECTIVES: Define what we're optimizing for
   # ========================================================================
   # This is a COMBINED objective: one execution produces BOTH f1 and f2
   # The script outputs: {"f1": value1, "f2": value2}
   # We extract each metric via "metric_key"
   objectives:
     - name: "dtlz2_pareto"
       direction: "minimize"  # This applies to the combined objective as a whole
       
       # Objective plan: HOW to compute the objective(s)
       objective_plan:
         steps:
           stages:
             - name: "evaluate_objectives"
               script:
                 path: "examples/complete/scripts/dtlz2_problem.py"
                 output_file: "objectives.json"
                 timeout_sec: 600
               produces_objective: true
       
       # Metrics: Extract multiple values from ONE objective_plan execution
       # Instead of running the script twice (once for f1, once for f2),
       # we run it once and extract both metrics from the output
       metrics:
         - name: "f1"
           direction: "minimize"
           metric_key: "f1"  # Extract the "f1" key from output JSON
         
         - name: "f2"
           direction: "minimize"
           metric_key: "f2"  # Extract the "f2" key from output JSON
 
 # SECTION 2: SCHEDULER CONFIGURATION
 # How to run computations (global defaults)
 # ============================================================================
 scheduler:
   runner_type: "JobLibRunner"
   parameters:
     n_jobs: 4          # Use 4 parallel jobs by default
     backend: "loky"    # Use process-based parallelism
 
 # SECTION 3: WORKFLOW EXECUTION
 # How to execute the optimization (stages, parallelism, etc.)
 # ============================================================================
 workflows:
   - name: "dtlz2_optimization"
     description: "DTLZ2 evaluation workflow"
     
     # Workflow-level scheduler (overrides global scheduler)
     scheduler:
       runner_type: "JobLibRunner"
       parameters:
         n_jobs: 8      # Use 8 jobs for this workflow
         backend: "loky"
     
     # Branches: parallel execution paths
     branches:
       - name: "main"
         description: "Main evaluation branch"
         
         # Branch-level scheduler (overrides workflow scheduler)
         scheduler:
           runner_type: "JobLibRunner"
           parameters:
             n_jobs: 2      # Use 2 jobs for this branch
             backend: "loky"
         
         # Stages: sequential computation steps
         stages:
           - name: "evaluate_objectives"
             description: "Evaluate DTLZ2 objectives for design points"
             
             # Individual jobs for this stage
             jobs:
               - name: "dtlz2_eval"
                 command: "python examples/complete/scripts/dtlz2_problem.py"
                 outputs:
                   - path: "objectives.json"
                     format: "json"
             
             # Job factory: expand one job into many parallel copies
             # Creates 4 parallel job instances (one per design point)
             job_factory:
               type: "range"
               params:
                 n: 4
             
             # Parallelism policies for this stage
             parallelism:
               max_concurrent: 4      # Run max 4 jobs in parallel
               retry_max: 2           # Retry failed jobs up to 2 times
               timeout_sec: 300       # Kill job if it takes >300 seconds
             
             # Stage-level scheduler (overrides branch scheduler)
             scheduler:
               runner_type: "JobLibRunner"
               parameters:
                 n_jobs: 4            # Stage can override branch's n_jobs=2
                 backend: "loky"
             
             # Output artifacts
             outputs:
               - path: "objectives.json"
                 format: "json"

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