v0.6.0 - Population Management

Overview

This release completes the Structural Phase by refactoring population_monitor.erl and related modules. The focus is on clarifying state management, documenting the evolutionary loop, and improving species handling.

Phase: Structural Duration: 2 weeks Prerequisites: v0.5.0 (genome_mutator, selection utilities refactored)

Objectives

1. Refactor population_monitor.erl state record and handlers
2. Document evolutionary generation loop
3. Clarify species management and selection
4. Integrate with refactored genome_mutator
5. Add comprehensive integration tests

Modules to Refactor

1. population_monitor.erl (1,142 lines)

Analysis Reference: Section 3.5 of DXNN2_CODEBASE_ANALYSIS.md (lines 436-441)

Issues to Address:

• 18-field state record with unclear purposes
• "activeAgent_IdPs" naming convention
• Complex gen_server handlers
• Generation loop not well documented

Tasks:

1. Clarify and rename state record fields

   %% Before: cryptic names
   -record(state, {
       op_mode,
       population_id,
       activeAgent_IdPs=[],      % Unclear
       agent_ids=[],
       tot_agents,               % Abbreviated
       agents_left,
       op_tag,
       ...
   }).
   
   %% After: clear names
   -record(population_state, {
       operation_mode,           % gt | validation | test
       population_id,
       active_agent_processes=[], % [{AgentId, Pid}]
       agent_ids=[],
       total_agents,
       remaining_agents,
       operation_tag,
       generation_count = 0,
       fitness_goal,
       max_generations,
       evaluation_limit,
       selection_algorithm,
       fitness_postprocessor,
       evolutionary_strategy,
       current_best_fitness,
       specie_size_limit,
       species_map = #{},        % SpecieId -> [AgentIds]
       timestamp_started,
       survival_rate
   }).

2. Document evolutionary generation loop

   %% @doc Main evolutionary loop
   %%
   %% Each generation follows these steps:
   %% 1. Spawn Phase: Launch all agent processes in parallel
   %% 2. Evaluation Phase: Agents run sense-think-act cycles
   %% 3. Collection Phase: Gather fitness results
   %% 4. Selection Phase: Select survivors based on fitness
   %% 5. Reproduction Phase: Replicate and mutate survivors
   %% 6. Speciation Phase: Update species assignments
   %% 7. Termination Check: Goal reached or max generations?
   %%
   %% == Fitness Collection ==
   %% As each agent completes, it reports fitness to monitor.
   %% Monitor accumulates results and triggers next phase
   %% when all agents complete.
   %%
   %% == Selection Algorithms ==
   %% - competition: Tournament selection
   %% - top_x: Top X% survive
   %% - steady_state: Continuous replacement

3. Decompose gen_server handlers

   %% Before: large handle_cast with multiple patterns
   
   %% After: separate handler functions
   handle_cast({agent_terminated, AgentId, Fitness}, State) ->
       handle_agent_termination(AgentId, Fitness, State);
   
   handle_cast({evaluation_complete, AgentId, Fitness, Cycles}, State) ->
       handle_evaluation_result(AgentId, Fitness, Cycles, State);
   
   handle_cast(next_generation, State) ->
       handle_generation_advance(State).
   
   %% Handler implementations
   handle_agent_termination(AgentId, Fitness, State) ->
       UpdatedActive = remove_agent(AgentId, State#population_state.active_agent_processes),
       UpdatedState = State#population_state{
           active_agent_processes = UpdatedActive,
           remaining_agents = State#population_state.remaining_agents - 1
       },
       case UpdatedState#population_state.remaining_agents of
           0 -> trigger_selection_phase(UpdatedState);
           _ -> {noreply, UpdatedState}
       end.

4. Add type specifications for callbacks

   -spec init(Args) -> {ok, State} when
       Args :: population_init_args(),
       State :: population_state().
   
   -spec handle_call(Request, From, State) -> {reply, Reply, State} when
       Request :: population_request(),
       From :: {pid(), term()},
       State :: population_state(),
       Reply :: population_response().

5. Extract species management functions

   %% Create species_manager.erl or inline clear functions
   
   -spec assign_to_species(agent_id(), population_state()) -> specie_id().
   assign_to_species(AgentId, State) ->
       %% Determine species based on behavioral signature
       Agent = genotype:read({agent, AgentId}),
       Signature = calculate_behavioral_signature(Agent),
       find_matching_species(Signature, State#population_state.species_map).
   
   -spec update_species_fitness(specie_id(), [fitness()], population_state()) ->
       population_state().
   update_species_fitness(SpecieId, Fitnesses, State) ->
       %% Update species average fitness for selection
       AvgFitness = lists:sum(Fitnesses) / length(Fitnesses),
       update_specie_record(SpecieId, AvgFitness, State).

6. Add module documentation

   %% @module population_monitor
   %% @doc Population-level evolutionary process manager
   %%
   %% This gen_server manages the evolutionary process for a population
   %% of neural network agents. It coordinates:
   %%
   %% - Agent lifecycle (spawn, evaluate, terminate)
   %% - Fitness collection and aggregation
   %% - Selection and reproduction
   %% - Species formation and management
   %% - Termination condition checking
   %%
   %% == Population Hierarchy ==
   %% Population contains multiple species.
   %% Species contains multiple agents with similar behavior.
   %% Agents compete within species for selection.
   %%
   %% == Multi-Objective Fitness ==
   %% Fitness is a vector [F1, F2, ...] supporting multi-objective
   %% optimization with different aggregation strategies.

2. selection_algorithm.erl

Tasks:

1. Document selection strategies
2. Add type specifications
3. Ensure integration with refactored population_monitor

3. fitness_postprocessor.erl

Tasks:

1. Document postprocessing strategies
2. Add type specifications
3. Clarify multi-objective handling

4. genotype.erl (Integration)

Tasks:

1. Add consistent read/write patterns
2. Document database access patterns
3. Ensure clean integration with population_monitor

Tests to Write

population_monitor_test.erl

-module(population_monitor_test).
-include_lib("eunit/include/eunit.hrl").
-include("records.hrl").

%% ============================================================================
%% State initialization tests
%% ============================================================================

initial_state_test() ->
    State = #population_state{
        population_id = test_pop,
        operation_mode = gt,
        total_agents = 10
    },
    ?assertEqual(gt, State#population_state.operation_mode),
    ?assertEqual(10, State#population_state.total_agents).

%% ============================================================================
%% Agent management tests
%% ============================================================================

track_active_agent_test() ->
    State = #population_state{
        active_agent_processes = []
    },
    AgentId = {{0.1, 0.2}, agent},
    Pid = self(),

    Updated = add_active_agent(AgentId, Pid, State),

    ?assertEqual([{AgentId, Pid}], Updated#population_state.active_agent_processes).

remove_active_agent_test() ->
    AgentId = {{0.1, 0.2}, agent},
    State = #population_state{
        active_agent_processes = [{AgentId, self()}],
        remaining_agents = 1
    },

    Updated = remove_active_agent(AgentId, State),

    ?assertEqual([], Updated#population_state.active_agent_processes).

%% ============================================================================
%% Fitness collection tests
%% ============================================================================

collect_fitness_test() ->
    %% Test fitness accumulation
    State = #population_state{
        remaining_agents = 3
    },

    %% Simulate three agents completing
    {_, State1} = handle_agent_fitness(agent1, [1.0], State),
    {_, State2} = handle_agent_fitness(agent2, [2.0], State1),
    {Result, State3} = handle_agent_fitness(agent3, [3.0], State2),

    %% After all agents, should trigger selection
    ?assertEqual(trigger_selection, Result),
    ?assertEqual(0, State3#population_state.remaining_agents).

%% ============================================================================
%% Selection phase tests
%% ============================================================================

selection_phase_survivors_test() ->
    %% Test that selection produces correct number of survivors
    Fitnesses = [
        {agent1, [5.0]},
        {agent2, [3.0]},
        {agent3, [4.0]},
        {agent4, [1.0]},
        {agent5, [2.0]}
    ],
    SurvivalRate = 0.4,  % Keep top 40%

    Survivors = population_monitor:select_survivors(Fitnesses, SurvivalRate),

    ?assertEqual(2, length(Survivors)),
    ?assert(lists:member(agent1, Survivors)),
    ?assert(lists:member(agent3, Survivors)).

%% ============================================================================
%% Generation advancement tests
%% ============================================================================

generation_increment_test() ->
    State = #population_state{
        generation_count = 5
    },

    Updated = advance_generation(State),

    ?assertEqual(6, Updated#population_state.generation_count).

termination_by_goal_test() ->
    %% Should terminate when fitness goal reached
    State = #population_state{
        fitness_goal = [10.0],
        current_best_fitness = [15.0]
    },

    ?assertEqual(true, should_terminate(State)).

termination_by_generations_test() ->
    %% Should terminate when max generations reached
    State = #population_state{
        max_generations = 100,
        generation_count = 100
    },

    ?assertEqual(true, should_terminate(State)).

%% ============================================================================
%% Integration tests
%% ============================================================================

full_generation_cycle_test() ->
    %% Setup test population
    {ok, PopMonPid} = test_helpers:start_test_population_monitor(),

    %% Run one generation
    gen_server:cast(PopMonPid, start_evaluation),

    %% Wait for generation to complete
    receive
        {generation_complete, GenNum, BestFitness} ->
            ?assertEqual(1, GenNum),
            ?assert(is_list(BestFitness))
    after 5000 ->
        ?assert(false)  % Timeout
    end,

    test_helpers:stop_population_monitor(PopMonPid).

species_management_test.erl

-module(species_management_test).
-include_lib("eunit/include/eunit.hrl").

%% ============================================================================
%% Species assignment tests
%% ============================================================================

assign_new_species_test() ->
    %% First agent creates new species
    SpeciesMap = #{},
    AgentSignature = [0.5, 0.3, 0.8],

    {SpecieId, UpdatedMap} = assign_species(AgentSignature, SpeciesMap),

    ?assert(maps:is_key(SpecieId, UpdatedMap)).

assign_existing_species_test() ->
    %% Similar agent joins existing species
    ExistingSignature = [0.5, 0.3, 0.8],
    SpeciesMap = #{specie1 => {ExistingSignature, [agent1]}},

    SimilarSignature = [0.51, 0.29, 0.79],  % Within threshold
    {SpecieId, _} = assign_species(SimilarSignature, SpeciesMap),

    ?assertEqual(specie1, SpecieId).

%% ============================================================================
%% Species fitness tests
%% ============================================================================

species_average_fitness_test() ->
    Fitnesses = [[1.0], [2.0], [3.0]],
    AvgFitness = calculate_species_average(Fitnesses),
    ?assertEqual([2.0], AvgFitness).

Documentation Requirements

Required Documentation

1. population_monitor.erl

   • Module overview with evolutionary cycle
   • State record field documentation
   • Selection algorithm descriptions
   • Termination conditions

2. selection_algorithm.erl

   • Each selection strategy documented
   • Mathematical basis

3. fitness_postprocessor.erl

   • Postprocessing strategies
   • Multi-objective handling

Documentation Checklist

• [ ] Evolutionary loop fully documented
• [ ] State record all fields explained
• [ ] Selection algorithms described
• [ ] Species management explained
• [ ] Termination conditions listed
• [ ] All function specs complete

Quality Gates

v0.6.0 Acceptance Criteria

1. State Management

   • [ ] State record has clear field names
   • [ ] No "IdPs" naming
   • [ ] Species map properly typed

2. Code Quality

   • [ ] gen_server handlers decomposed
   • [ ] No handler exceeds 30 lines
   • [ ] Species logic extracted

3. Test Coverage

   • [ ] population_monitor.erl: 80%+ coverage
   • [ ] Full generation cycle tested
   • [ ] Selection algorithms tested

4. Integration

   • [ ] Works with refactored genome_mutator
   • [ ] Exoself integration verified
   • [ ] End-to-end evolution test passes

5. Static Analysis

   • [ ] Zero dialyzer warnings

Known Limitations

• Complex species dynamics simplified
• Multi-objective optimization basic
• Performance not optimized for large populations

Next Steps

After v0.6.0 completion:

1. v0.7.0 begins Robustness Phase
2. Structural Phase complete
3. Focus shifts to bug fixes and error handling

Implementation Notes

Generation Cycle Flow

start_evaluation
    |
    v
spawn_all_agents
    |
    v
[wait for all agents to complete]
    |
    v
collect_all_fitness
    |
    v
postprocess_fitness
    |
    v
select_survivors
    |
    v
replicate_and_mutate
    |
    v
update_species
    |
    v
check_termination -> [done] | [continue -> start_evaluation]

Selection Algorithm Integration

select_survivors(AgentFitnesses, State) ->
    Algorithm = State#population_state.selection_algorithm,
    SurvivalRate = State#population_state.survival_rate,

    selection_algorithm:Algorithm(AgentFitnesses, SurvivalRate).

Multi-Objective Fitness Handling

%% Fitness is always a vector
-type fitness() :: [float()].

%% Compare using dominance or weighted sum
compare_fitness(F1, F2, Strategy) ->
    case Strategy of
        pareto -> pareto_dominates(F1, F2);
        weighted_sum -> sum(F1) > sum(F2);
        lexicographic -> lexicographic_compare(F1, F2)
    end.

Dependencies

External Dependencies

• gen_server (OTP)
• Mnesia for persistence

Internal Dependencies

• v0.5.0: genome_mutator, selection_utils
• v0.4.0: exoself
• v0.1.0: types, test infrastructure

Effort Estimate

Risks

Version: 0.6.0 Phase: Structural Status: Planned