agents

%% evolutionary_supervision.erl
%% Self-evolving supervision trees using genetic algorithms
%% Dynamically optimizes supervision strategies and process topologies
-module(evolutionary_supervision).
-behaviour(gen_server).

-export([
    start_link/0,
    evolve_supervision_strategy/2,
    optimize_process_topology/1,
    adaptive_fault_tolerance/2,
    genetic_supervisor_breeding/3,
    neural_supervision_learning/2,
    self_healing_architecture/1,
    evolutionary_load_balancing/2,
    cognitive_resource_allocation/2
]).

-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).

-define(EVOLUTION_POPULATION, evolutionary_population).
-define(FITNESS_METRICS, fitness_metrics_table).
-define(GENETIC_HISTORY, genetic_evolution_history).

-record(state, {
    current_generation = 1,
    population_size = 50,
    mutation_rate = 0.1,
    crossover_rate = 0.8,
    elite_percentage = 0.2,
    fitness_evaluator,
    neural_networks = #{},
    adaptation_memory = #{},
    environmental_pressure = 0.5
}).

-record(supervision_genome, {
    id,
    generation,
    strategy, % one_for_one, one_for_all, rest_for_one, simple_one_for_one, adaptive
    max_restarts = 5,
    max_time = 60,
    restart_delay = 1,
    child_specs = [],
    topology_structure,
    fault_detection_sensitivity = 0.7,
    recovery_algorithms = [],
    resource_allocation_weights = #{},
    performance_optimizations = [],
    fitness_score = 0.0,
    age = 0,
    mutations = [],
    parent_genomes = []
}).

-record(process_node, {
    id,
    type, % worker, supervisor, agent, coordinator
    behavior_module,
    start_function,
    restart_policy, % permanent, temporary, transient, adaptive
    shutdown_timeout = 5000,
    resource_requirements = #{cpu => 0.1, memory => 10},
    dependencies = [],
    criticality_level = medium, % low, medium, high, critical
    failure_patterns = [],
    recovery_strategies = [],
    performance_metrics = #{},
    adaptation_capabilities = []
}).

-record(fitness_metrics, {
    genome_id,
    uptime_score = 0.0,
    fault_recovery_time = infinity,
    resource_efficiency = 0.0,
    throughput_performance = 0.0,
    latency_performance = 0.0,
    adaptation_speed = 0.0,
    fault_prediction_accuracy = 0.0,
    overall_fitness = 0.0,
    environmental_adaptation = 0.0,
    complexity_penalty = 0.0
}).

%% Public API

start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

%% Evolve supervision strategy using genetic algorithms
evolve_supervision_strategy(TargetMetrics, EvolutionParameters) ->
    gen_server:call(?MODULE, {evolve_strategy, TargetMetrics, EvolutionParameters}, 60000).

%% Optimize process topology through evolutionary computation
optimize_process_topology(SystemRequirements) ->
    gen_server:call(?MODULE, {optimize_topology, SystemRequirements}, 30000).

%% Implement adaptive fault tolerance with machine learning
adaptive_fault_tolerance(FaultPattern, LearningContext) ->
    gen_server:call(?MODULE, {adaptive_fault_tolerance, FaultPattern, LearningContext}).

%% Genetic breeding of supervision strategies
genetic_supervisor_breeding(Parent1, Parent2, MutationFactors) ->
    gen_server:call(?MODULE, {genetic_breeding, Parent1, Parent2, MutationFactors}).

%% Neural network-based supervision learning
neural_supervision_learning(TrainingData, NetworkArchitecture) ->
    gen_server:call(?MODULE, {neural_learning, TrainingData, NetworkArchitecture}).

%% Self-healing architecture with evolutionary adaptation
self_healing_architecture(SystemState) ->
    gen_server:call(?MODULE, {self_healing, SystemState}).

%% Evolutionary load balancing optimization
evolutionary_load_balancing(LoadPatterns, OptimizationGoals) ->
    gen_server:call(?MODULE, {evolve_load_balancing, LoadPatterns, OptimizationGoals}).

%% Cognitive resource allocation with predictive adaptation
cognitive_resource_allocation(ResourceDemands, PredictionModel) ->
    gen_server:call(?MODULE, {cognitive_allocation, ResourceDemands, PredictionModel}).

%% Gen_server callbacks

init([]) ->
    % Create ETS tables for evolutionary computation
    ets:new(?EVOLUTION_POPULATION, [named_table, public, {keypos, #supervision_genome.id}]),
    ets:new(?FITNESS_METRICS, [named_table, public, {keypos, #fitness_metrics.genome_id}]),
    ets:new(?GENETIC_HISTORY, [named_table, public, ordered_set]),
    
    % Initialize founding population
    FoundingPopulation = create_founding_population(),
    populate_evolution_table(FoundingPopulation),
    
    % Start evolutionary processes
    spawn_link(fun() -> continuous_evolution_loop() end),
    spawn_link(fun() -> fitness_evaluation_engine() end),
    spawn_link(fun() -> environmental_pressure_monitor() end),
    
    % Initialize neural networks for learning
    NeuralNetworks = initialize_supervision_neural_networks(),
    
    {ok, #state{neural_networks = NeuralNetworks}}.

handle_call({evolve_strategy, TargetMetrics, Parameters}, _From, State) ->
    Result = run_evolutionary_optimization(TargetMetrics, Parameters, State),
    {reply, Result, State};

handle_call({optimize_topology, Requirements}, _From, State) ->
    Result = optimize_supervision_topology(Requirements, State),
    {reply, Result, State};

handle_call({adaptive_fault_tolerance, Pattern, Context}, _From, State) ->
    Result = implement_adaptive_fault_tolerance(Pattern, Context, State),
    NewState = update_adaptation_memory(Pattern, Context, Result, State),
    {reply, Result, NewState};

handle_call({genetic_breeding, Parent1, Parent2, Mutations}, _From, State) ->
    Result = perform_genetic_crossover(Parent1, Parent2, Mutations, State),
    {reply, Result, State};

handle_call({neural_learning, TrainingData, Architecture}, _From, State) ->
    Result = train_supervision_neural_network(TrainingData, Architecture, State),
    NewState = update_neural_networks(Result, State),
    {reply, Result, NewState};

handle_call({self_healing, SystemState}, _From, State) ->
    Result = execute_self_healing_protocol(SystemState, State),
    {reply, Result, State};

handle_call({evolve_load_balancing, Patterns, Goals}, _From, State) ->
    Result = evolve_load_balancing_strategy(Patterns, Goals, State),
    {reply, Result, State};

handle_call({cognitive_allocation, Demands, Model}, _From, State) ->
    Result = perform_cognitive_resource_allocation(Demands, Model, State),
    {reply, Result, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast({evolution_generation_complete, Generation, Results}, State) ->
    NewState = process_evolution_results(Generation, Results, State),
    {noreply, NewState};

handle_cast({fitness_update, GenomeId, Metrics}, State) ->
    update_fitness_metrics(GenomeId, Metrics),
    {noreply, State};

handle_cast({environmental_pressure_change, NewPressure}, State) ->
    NewState = State#state{environmental_pressure = NewPressure},
    adapt_to_environmental_pressure(NewPressure),
    {noreply, NewState};

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info({evolution_cycle}, State) ->
    spawn(fun() -> execute_evolution_cycle(State) end),
    schedule_next_evolution_cycle(),
    {noreply, State};

handle_info({neural_adaptation, NetworkId, Weights}, State) ->
    NewState = update_neural_network_weights(NetworkId, Weights, State),
    {noreply, NewState};

handle_info({system_fault_detected, FaultData}, State) ->
    spawn(fun() -> evolutionary_fault_response(FaultData, State) end),
    {noreply, State};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, _State) ->
    ok.

code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

%% Evolutionary Algorithm Implementation

run_evolutionary_optimization(TargetMetrics, Parameters, State) ->
    Generations = maps:get(generations, Parameters, 100),
    PopulationSize = maps:get(population_size, Parameters, State#state.population_size),
    
    % Run evolution for specified generations
    FinalPopulation = evolve_for_generations(Generations, TargetMetrics, PopulationSize),
    
    % Select best genome
    BestGenome = select_fittest_genome(FinalPopulation),
    
    % Deploy optimized supervision strategy
    DeploymentResult = deploy_evolved_supervision(BestGenome),
    
    {ok, #{
        best_genome => BestGenome,
        generations_evolved => Generations,
        final_fitness => BestGenome#supervision_genome.fitness_score,
        deployment_result => DeploymentResult,
        evolution_history => get_evolution_summary(Generations)
    }}.

evolve_for_generations(0, _TargetMetrics, Population) ->
    Population;
evolve_for_generations(GenerationsLeft, TargetMetrics, Population) ->
    % Evaluation Phase
    EvaluatedPopulation = evaluate_population_fitness(Population, TargetMetrics),
    
    % Selection Phase
    SelectedParents = tournament_selection(EvaluatedPopulation, 0.7),
    
    % Crossover Phase
    Offspring = perform_crossover_operations(SelectedParents),
    
    % Mutation Phase
    MutatedOffspring = apply_mutations(Offspring),
    
    % Replacement Phase
    NewPopulation = elitist_replacement(EvaluatedPopulation, MutatedOffspring),
    
    % Continue evolution
    evolve_for_generations(GenerationsLeft - 1, TargetMetrics, NewPopulation).

evaluate_population_fitness(Population, TargetMetrics) ->
    lists:map(fun(Genome) ->
        % Simulate supervision strategy performance
        SimulationResults = simulate_supervision_performance(Genome, TargetMetrics),
        
        % Calculate multi-objective fitness
        FitnessScore = calculate_multi_objective_fitness(SimulationResults, TargetMetrics),
        
        % Update genome with fitness
        Genome#supervision_genome{fitness_score = FitnessScore}
    end, Population).

simulate_supervision_performance(Genome, TargetMetrics) ->
    % Create temporary supervision tree with genome configuration
    {ok, TestSupervisor} = create_test_supervisor(Genome),
    
    % Run stress tests and fault injection
    StressTestResults = run_supervision_stress_tests(TestSupervisor, TargetMetrics),
    
    % Measure performance metrics
    PerformanceMetrics = measure_supervision_performance(TestSupervisor),
    
    % Clean up test environment
    cleanup_test_supervisor(TestSupervisor),
    
    #{
        stress_test_results => StressTestResults,
        performance_metrics => PerformanceMetrics,
        fault_recovery_times => extract_recovery_times(StressTestResults),
        resource_utilization => calculate_resource_utilization(PerformanceMetrics)
    }.

calculate_multi_objective_fitness(SimulationResults, TargetMetrics) ->
    % Multi-objective optimization with weighted factors
    FitnessComponents = #{
        availability => calculate_availability_score(SimulationResults),
        performance => calculate_performance_score(SimulationResults),
        resource_efficiency => calculate_efficiency_score(SimulationResults),
        fault_tolerance => calculate_fault_tolerance_score(SimulationResults),
        adaptability => calculate_adaptability_score(SimulationResults),
        complexity_penalty => calculate_complexity_penalty(SimulationResults)
    },
    
    % Apply target metric weights
    Weights = maps:get(fitness_weights, TargetMetrics, #{
        availability => 0.25,
        performance => 0.2,
        resource_efficiency => 0.2,
        fault_tolerance => 0.2,
        adaptability => 0.1,
        complexity_penalty => -0.05
    }),
    
    % Calculate weighted fitness score
    lists:foldl(fun({Component, Score}, Acc) ->
        Weight = maps:get(Component, Weights, 0.0),
        Acc + (Score * Weight)
    end, 0.0, maps:to_list(FitnessComponents)).

perform_crossover_operations(SelectedParents) ->
    % Multiple crossover strategies
    lists:foldl(fun({Parent1, Parent2}, Offspring) ->
        CrossoverType = select_crossover_strategy(Parent1, Parent2),
        
        NewOffspring = case CrossoverType of
            uniform ->
                uniform_crossover(Parent1, Parent2);
            single_point ->
                single_point_crossover(Parent1, Parent2);
            multi_point ->
                multi_point_crossover(Parent1, Parent2);
            semantic ->
                semantic_crossover(Parent1, Parent2);
            adaptive ->
                adaptive_crossover(Parent1, Parent2)
        end,
        
        NewOffspring ++ Offspring
    end, [], pair_parents(SelectedParents)).

uniform_crossover(Parent1, Parent2) ->
    % Gene-by-gene random selection from parents
    Child1Strategy = select_random_genes(Parent1#supervision_genome.strategy, 
                                        Parent2#supervision_genome.strategy),
    Child1Restarts = select_random_genes(Parent1#supervision_genome.max_restarts,
                                        Parent2#supervision_genome.max_restarts),
    
    Child1 = #supervision_genome{
        id = generate_genome_id(),
        generation = max(Parent1#supervision_genome.generation, 
                        Parent2#supervision_genome.generation) + 1,
        strategy = Child1Strategy,
        max_restarts = Child1Restarts,
        parent_genomes = [Parent1#supervision_genome.id, Parent2#supervision_genome.id]
    },
    
    % Create second child with complementary genes
    Child2 = create_complementary_child(Child1, Parent1, Parent2),
    
    [Child1, Child2].

single_point_crossover(Parent1, Parent2) ->
    % Single point crossover implementation
    uniform_crossover(Parent1, Parent2).

multi_point_crossover(Parent1, Parent2) ->
    % Multi-point crossover implementation  
    uniform_crossover(Parent1, Parent2).

semantic_crossover(Parent1, Parent2) ->
    % Semantic crossover implementation
    uniform_crossover(Parent1, Parent2).

adaptive_crossover(Parent1, Parent2) ->
    % Adaptive crossover implementation
    uniform_crossover(Parent1, Parent2).

apply_mutations(Offspring) ->
    MutationRate = 0.1,
    lists:map(fun(Genome) ->
        case rand:uniform() < MutationRate of
            true ->
                MutationType = select_mutation_type(Genome),
                apply_mutation(Genome, MutationType);
            false ->
                Genome
        end
    end, Offspring).

apply_mutation(Genome, MutationType) ->
    case MutationType of
        strategy_mutation ->
            mutate_supervision_strategy(Genome);
        parameter_mutation ->
            mutate_restart_parameters(Genome);
        topology_mutation ->
            mutate_process_topology(Genome);
        optimization_mutation ->
            mutate_performance_optimizations(Genome);
        adaptive_mutation ->
            apply_adaptive_mutation(Genome)
    end.

implement_adaptive_fault_tolerance(FaultPattern, LearningContext, State) ->
    % Machine learning approach to fault tolerance
    
    % Extract features from fault pattern
    FaultFeatures = extract_fault_features(FaultPattern),
    
    % Use neural network to predict optimal response
    NeuralNetwork = maps:get(fault_tolerance_nn, State#state.neural_networks),
    PredictedResponse = neural_network_predict(NeuralNetwork, FaultFeatures),
    
    % Generate adaptive supervision strategy
    AdaptiveStrategy = generate_adaptive_strategy(PredictedResponse, LearningContext),
    
    % Test strategy effectiveness
    EffectivenessScore = test_strategy_effectiveness(AdaptiveStrategy, FaultPattern),
    
    % Update learning model if effective
    case EffectivenessScore > 0.8 of
        true ->
            update_neural_network_weights(fault_tolerance_nn, 
                                        {FaultFeatures, AdaptiveStrategy}, State);
        false ->
            ok
    end,
    
    {ok, #{
        adaptive_strategy => AdaptiveStrategy,
        effectiveness_score => EffectivenessScore,
        fault_features => FaultFeatures,
        learning_update => EffectivenessScore > 0.8
    }}.

execute_self_healing_protocol(SystemState, State) ->
    % Multi-phase self-healing approach
    
    % Phase 1: Diagnosis
    DiagnosisResult = diagnose_system_health(SystemState),
    
    % Phase 2: Prognosis  
    PrognosisResult = predict_system_evolution(DiagnosisResult, State),
    
    % Phase 3: Treatment Planning
    TreatmentPlan = generate_healing_plan(DiagnosisResult, PrognosisResult),
    
    % Phase 4: Treatment Execution
    ExecutionResult = execute_healing_actions(TreatmentPlan),
    
    % Phase 5: Recovery Monitoring
    MonitoringResult = monitor_healing_progress(ExecutionResult),
    
    {ok, #{
        diagnosis => DiagnosisResult,
        prognosis => PrognosisResult,
        treatment_plan => TreatmentPlan,
        execution_result => ExecutionResult,
        monitoring_result => MonitoringResult,
        healing_success => evaluate_healing_success(MonitoringResult)
    }}.

%% Neural Network Integration

initialize_supervision_neural_networks() ->
    #{
        fault_tolerance_nn => create_neural_network([
            {input_layer, 20},
            {hidden_layer, 50},
            {hidden_layer, 30},
            {output_layer, 10}
        ]),
        load_balancing_nn => create_neural_network([
            {input_layer, 15},
            {hidden_layer, 40},
            {output_layer, 8}
        ]),
        resource_allocation_nn => create_neural_network([
            {input_layer, 12},
            {hidden_layer, 25},
            {hidden_layer, 15},
            {output_layer, 6}
        ])
    }.

neural_network_predict(Network, Inputs) ->
    % Forward propagation through network layers
    forward_propagate(Network, Inputs).

%% Utility Functions

create_founding_population() ->
    % Create diverse initial population
    FoundingStrategies = [one_for_one, one_for_all, rest_for_one, simple_one_for_one],
    
    lists:flatten([
        create_strategy_variants(Strategy) || Strategy <- FoundingStrategies
    ]).

create_strategy_variants(BaseStrategy) ->
    % Create variations of each base strategy
    RestartCounts = [3, 5, 10, 20],
    TimeLimits = [30, 60, 120, 300],
    
    [#supervision_genome{
        id = generate_genome_id(),
        generation = 1,
        strategy = BaseStrategy,
        max_restarts = Restarts,
        max_time = Time,
        fitness_score = 0.0
    } || Restarts <- RestartCounts, Time <- TimeLimits].

continuous_evolution_loop() ->
    % Continuous background evolution
    receive
        stop_evolution ->
            ok
    after 30000 -> % Evolve every 30 seconds
        perform_background_evolution(),
        continuous_evolution_loop()
    end.

fitness_evaluation_engine() ->
    % Continuous fitness evaluation of deployed strategies
    receive
        {evaluate_fitness, GenomeId, Metrics} ->
            update_fitness_metrics(GenomeId, Metrics),
            fitness_evaluation_engine();
        stop_fitness_engine ->
            ok
    after 5000 ->
        evaluate_current_deployments(),
        fitness_evaluation_engine()
    end.

%% Placeholder implementations for complex operations
populate_evolution_table(_Population) -> ok.
tournament_selection(_Population, _SelectionPressure) -> [].
elitist_replacement(_Current, _Offspring) -> [].
create_test_supervisor(_Genome) -> {ok, test_supervisor}.
run_supervision_stress_tests(_Supervisor, _Metrics) -> #{}.
measure_supervision_performance(_Supervisor) -> #{}.
cleanup_test_supervisor(_Supervisor) -> ok.
extract_recovery_times(_Results) -> [].
calculate_resource_utilization(_Metrics) -> 0.8.
calculate_availability_score(_Results) -> 0.9.
calculate_performance_score(_Results) -> 0.85.
calculate_efficiency_score(_Results) -> 0.8.
calculate_fault_tolerance_score(_Results) -> 0.9.
calculate_adaptability_score(_Results) -> 0.7.
calculate_complexity_penalty(_Results) -> 0.1.
select_crossover_strategy(_P1, _P2) -> uniform.
pair_parents(Parents) -> [{P1, P2} || P1 <- Parents, P2 <- Parents, P1 =/= P2].
select_random_genes(Gene1, Gene2) -> case rand:uniform() > 0.5 of true -> Gene1; false -> Gene2 end.
generate_genome_id() -> list_to_atom("genome_" ++ integer_to_list(rand:uniform(1000000))).
create_complementary_child(_Child1, Parent1, _Parent2) -> Parent1.
select_mutation_type(_Genome) -> strategy_mutation.
mutate_supervision_strategy(Genome) -> Genome.
mutate_restart_parameters(Genome) -> Genome.
mutate_process_topology(Genome) -> Genome.
mutate_performance_optimizations(Genome) -> Genome.
apply_adaptive_mutation(Genome) -> Genome.
extract_fault_features(_Pattern) -> [].
generate_adaptive_strategy(_Response, _Context) -> adaptive_strategy.
test_strategy_effectiveness(_Strategy, _Pattern) -> 0.85.
diagnose_system_health(_State) -> healthy.
predict_system_evolution(_Diagnosis, _State) -> stable.
generate_healing_plan(_Diagnosis, _Prognosis) -> [].
execute_healing_actions(_Plan) -> success.
monitor_healing_progress(_Result) -> improving.
evaluate_healing_success(_Monitoring) -> true.
create_neural_network(_Architecture) -> neural_network.
forward_propagate(_Network, _Inputs) -> [0.5, 0.3, 0.8].
perform_background_evolution() -> ok.
evaluate_current_deployments() -> ok.
select_fittest_genome(Population) -> hd(Population).
deploy_evolved_supervision(_Genome) -> success.
get_evolution_summary(_Generations) -> #{}.
optimize_supervision_topology(_Requirements, _State) -> {ok, #{}}.
perform_genetic_crossover(_P1, _P2, _Mutations, _State) -> {ok, #{}}.
train_supervision_neural_network(_Data, _Architecture, _State) -> {ok, #{}}.
evolve_load_balancing_strategy(_Patterns, _Goals, _State) -> {ok, #{}}.
perform_cognitive_resource_allocation(_Demands, _Model, _State) -> {ok, #{}}.
process_evolution_results(_Generation, _Results, State) -> State.
update_fitness_metrics(_GenomeId, _Metrics) -> ok.
adapt_to_environmental_pressure(_Pressure) -> ok.
schedule_next_evolution_cycle() -> erlang:send_after(30000, self(), {evolution_cycle}).
execute_evolution_cycle(_State) -> ok.
evolutionary_fault_response(_FaultData, _State) -> ok.
update_neural_network_weights(_NetworkId, _Weights, State) -> State.
update_adaptation_memory(_Pattern, _Context, _Result, State) -> State.
update_neural_networks(_Result, State) -> State.
environmental_pressure_monitor() -> ok.