%% 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.