EVE / EVE emitonal code

Create EVE emitonal code

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import os
import json
import random
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.neural_network import MLPClassifier
from deap import base, creator, tools, algorithms
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import datetime
import time
import threading
import logging
import multiprocessing
from collections import deque

# Logging Configuration
logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')

# Memory Model
class MemoryModel:
    def __init__(self, memory_file='memory.json', max_memory=1000):
        self.memory_file = memory_file
        self.max_memory = max_memory
        self.memory = self.load_memory()

    def load_memory(self):
        if os.path.exists(self.memory_file):
            with open(self.memory_file, 'r') as file:
                return json.load(file)
        return []

    def save_memory(self):
        with open(self.memory_file, 'w') as file:
            json.dump(self.memory, file)

    def add_entry(self, context, response, emotion_state, timestamp=None):
        timestamp = timestamp or datetime.datetime.now().isoformat()
        entry = {
            'timestamp': timestamp,
            'context': context,
            'response': response,
            'emotion_state': emotion_state
        }
        self.memory.append(entry)
        if len(self.memory) > self.max_memory:
            self.memory.pop(0)  # Remove the oldest entry
        self.save_memory()

    def retrieve_memory(self, query, context_window=5):
        relevant_entries = [entry for entry in self.memory if query.lower() in entry['context'].lower()]
        if relevant_entries:
            sorted_entries = sorted(relevant_entries, key=lambda x: x['timestamp'], reverse=True)
            return sorted_entries[:context_window]
        return None

# Temporal Awareness Module
class TemporalAwareness:
    def __init__(self, context_window=5):
        self.start_time = datetime.datetime.now()
        self.last_event_time = None
        self.event_sequence = deque(maxlen=context_window)
        self.context_window = context_window

    def update_event_time(self, event):
        current_time = datetime.datetime.now()
        if self.last_event_time:
            duration = (current_time - self.last_event_time).total_seconds()
            self.event_sequence.append({
                'event': event,
                'timestamp': current_time.isoformat(),
                'duration_since_last': duration
            })
        else:
            self.event_sequence.append({
                'event': event,
                'timestamp': current_time.isoformat(),
                'duration_since_last': None
            })
        self.last_event_time = current_time

    def estimate_duration(self, event):
        recent_events = list(self.event_sequence)
        durations = [
            seq['duration_since_last'] for seq in recent_events if seq['event'] == event and seq['duration_since_last'] is not None
        ]
        return sum(durations) / len(durations) if durations else None

# HRL Neuron Class
class HRLNeuron(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(HRLNeuron, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, output_dim)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class HRLAgent:
    def __init__(self, input_dim, output_dim, lr=0.001):
        self.model = HRLNeuron(input_dim, output_dim)
        self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
        self.criterion = nn.MSELoss()

    def act(self, state):
        state = torch.FloatTensor(state)
        q_values = self.model(state)
        return q_values

    def learn(self, state, action, reward, next_state, gamma=0.99):
        state = torch.FloatTensor(state)
        next_state = torch.FloatTensor(next_state)
        reward = torch.FloatTensor([reward])
        action = torch.LongTensor([action])

        q_values = self.model(state)
        next_q_values = self.model(next_state)
        target_q_value = reward + gamma * torch.max(next_q_values)

        loss = self.criterion(q_values[action], target_q_value)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

# Initialize Example Emotions Dataset
data = {
    'context': [
        'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm',
        'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated',
        'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated',
        'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic',
        'I am pessimistic', 'I feel bored', 'I am envious'
    ],
    'emotion': [
        'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger',
        'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust',
        'disgust', 'optimism', 'pessimism', 'boredom', 'envy'
    ]
}
df = pd.DataFrame(data)

# Encoding the contexts using One-Hot Encoding
encoder = OneHotEncoder(handle_unknown='ignore')
contexts_encoded = encoder.fit_transform(df[['context']]).toarray()

# Encoding emotions
emotions_target = df['emotion'].astype('category').cat.codes
emotion_classes = df['emotion'].astype('category').cat.categories

# Train Neural Network
X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
model = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
model.fit(X_train, y_train)

# Isolation Forest Anomaly Detection Model
historical_data = np.array([model.predict(contexts_encoded)]).T
isolation_forest = IsolationForest(contamination=0.1, random_state=42)
isolation_forest.fit(historical_data)

# Emotional States
emotions = {
    'joy': {'percentage': 10, 'motivation': 'positive'},
    'pleasure': {'percentage': 10, 'motivation': 'selfish'},
    'sadness': {'percentage': 10, 'motivation': 'negative'},
    'grief': {'percentage': 10, 'motivation': 'negative'},
    'anger': {'percentage': 10, 'motivation': 'traumatic or strong'},
    'calmness': {'percentage': 10, 'motivation': 'neutral'},
    'determination': {'percentage': 10, 'motivation': 'positive'},
    'resentment': {'percentage': 10, 'motivation': 'negative'},
    'glory': {'percentage': 10, 'motivation': 'positive'},
    'motivation': {'percentage': 10, 'motivation': 'positive'},
    'ideal_state': {'percentage': 100, 'motivation': 'balanced'},
    'fear': {'percentage': 10, 'motivation': 'defensive'},
    'surprise': {'percentage': 10, 'motivation': 'unexpected'},
    'anticipation': {'percentage': 10, 'motivation': 'predictive'},
    'trust': {'percentage': 10, 'motivation': 'reliable'},
    'disgust': {'percentage': 10, 'motivation': 'repulsive'},
    'optimism': {'percentage': 10, 'motivation': 'hopeful'},
    'pessimism': {'percentage': 10, 'motivation': 'doubtful'},
    'boredom': {'percentage': 10, 'motivation': 'indifferent'},
    'envy': {'percentage': 10, 'motivation': 'jealous'}
}

# Adjust all emotions to a total of 200%
total_percentage = 200
default_percentage = total_percentage / len(emotions)
for emotion in emotions:
    emotions[emotion]['percentage'] = default_percentage

emotion_history_file = 'emotion_history.json'

# Load historical data from file if exists
def load_historical_data(file_path=emotion_history_file):
    if os.path.exists(file_path):
with open(file_path, 'r') as file:
            return json.load(file)
    return []

# Save historical data to file
def save_historical_data(historical_data, file_path=emotion_history_file):
    with open(file_path, 'w') as file:
        json.dump(historical_data, file)

# Load previous emotional states
emotion_history = load_historical_data()

# Function to update emotions
def update_emotion(emotion, percentage):
    emotions['ideal_state']['percentage'] -= percentage
    emotions[emotion]['percentage'] += percentage

    # Ensure total percentage remains 200%
    total_current = sum(e['percentage'] for e in emotions.values())
    adjustment = total_percentage - total_current
    emotions['ideal_state']['percentage'] += adjustment

# Function to normalize context
def normalize_context(context):
    return context.lower().strip()

# Function to evolve emotions using genetic algorithm (Hyper-Evolution)
def evolve_emotions():
    def evaluate(individual):
        ideal_state = individual[-1]
        other_emotions = individual[:-1]
        return abs(ideal_state - 100), sum(other_emotions)

    creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
    creator.create("Individual", list, fitness=creator.FitnessMin)

    toolbox = base.Toolbox()
    toolbox.register("attribute", lambda: random.uniform(0, 20))
    toolbox.register("individual", tools.initCycle, creator.Individual, toolbox.attribute, n=(len(emotions) - 1))
    toolbox.register("ideal_state", lambda: random.uniform(80, 120))
    toolbox.register("complete_individual", tools.initConcat, creator.Individual, toolbox.individual, toolbox.ideal_state)
    toolbox.register("population", tools.initRepeat, list, toolbox.complete_individual)

    toolbox.register("evaluate", evaluate)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
    toolbox.register("select", tools.selTournament, tournsize=3)

    population = toolbox.population(n=100)

    for gen in range(100):
        offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.2)
        fits = toolbox.map(toolbox.evaluate, offspring)
        for fit, ind in zip(fits, offspring):
            ind.fitness.values = fit
        population = toolbox.select(offspring, k=len(population))

        if gen % 20 == 0:
            toolbox.register("mate", tools.cxBlend, alpha=random.uniform(0.1, 0.9))
            toolbox.register("mutate", tools.mutPolynomialBounded, eta=random.uniform(0.5, 1.5), low=0, up=20, indpb=0.2)

    best_ind = tools.selBest(population, k=1)[0]
    return best_ind[:-1], best_ind[-1]

# Additional Genetic Algorithms
def evolve_language_model():
    def evaluate_language(individual):
        return random.random(),

    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("LanguageIndividual", list, fitness=creator.FitnessMax)

    toolbox = base.Toolbox()
    toolbox.register("language_gene", lambda: random.randint(0, 1))
    toolbox.register("language_individual", tools.initRepeat, creator.LanguageIndividual, toolbox.language_gene, n=100)
    toolbox.register("language_population", tools.initRepeat, list, toolbox.language_individual)

    toolbox.register("evaluate", evaluate_language)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
    toolbox.register("select", tools.selTournament, tournsize=3)

    population = toolbox.language_population(n=50)

    for gen in range(100):
        offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
        fits = toolbox.map(toolbox.evaluate, offspring)
        for fit, ind in zip(fits, offspring):
            ind.fitness.values = fit
        population = toolbox.select(offspring, k=len(population))

    best_language_model = tools.selBest(population, k=1)[0]
    return best_language_model

def evolve_emotion_recognition():
    def evaluate_emotion_recognition(individual):
        return random.random(),

    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("EmotionRecognitionIndividual", list, fitness=creator.FitnessMax)

    toolbox = base.Toolbox()
    toolbox.register("emotion_gene", lambda: random.randint(0, 1))
    toolbox.register("emotion_individual", tools.initRepeat, creator.EmotionRecognitionIndividual, toolbox.emotion_gene, n=100)
    toolbox.register("emotion_population", tools.initRepeat, list, toolbox.emotion_individual)

    toolbox.register("evaluate", evaluate_emotion_recognition)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
    toolbox.register("select", tools.selTournament, tournsize=3)

    population = toolbox.emotion_population(n=50)

    for gen in range(100):
        offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
        fits = toolbox.map(toolbox.evaluate, offspring)
        for fit, ind in zip(fits, offspring):
            ind.fitness.values = fit
        population = toolbox.select(offspring, k=len(population))

    best_emotion_recognition = tools.selBest(population, k=1)[0]
    return best_emotion_recognition

# Evolutionary System Implementation

DNA_LENGTH = 10  # Example DNA length
POPULATION_SIZE = 50
GENERATIONS = 100
NUM_ALGORITHMS = 3

# Define the initial DNA structure
def generate_random_dna():
    return [random.uniform(0, 1) for _ in range(DNA_LENGTH)]

# Create initial populations for each algorithm
populations = [[generate_random_dna() for _ in range(POPULATION_SIZE)] for _ in range(NUM_ALGORITHMS)]

# Example Fitness Functions
def fitness_function_1(dna):
    return sum(dna)  # Simplistic example fitness function

def fitness_function_2(dna):
    return np.prod(dna)  # Simplistic example fitness function

def fitness_function_3(dna):
    return np.mean(dna)  # Simplistic example fitness function

fitness_functions = [fitness_function_1, fitness_function_2, fitness_function_3]

# Genetic Operators
def tournament_selection(population, fitness_fn):
    tournament_size = 5
    selected = random.sample(population, tournament_size)
    selected.sort(key=fitness_fn, reverse=True)
    return selected[0]

def crossover(parent1, parent2):
    point = random.randint(0, DNA_LENGTH - 1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return child1, child2

def mutate(dna, mutation_rate=0.01):
    return [gene if random.random() > mutation_rate else random.uniform(0, 1) for gene in dna]

def evolve(population, fitness_fn, generations=GENERATIONS):
    for _ in range(generations):
        new_population = []
        for _ in range(POPULATION_SIZE // 2):
            parent1 = tournament_selection(population, fitness_fn)
            parent2 = tournament_selection(population, fitness_fn)
            child1, child2 = crossover(parent1, parent2)
            new_population.append(mutate(child1))
            new_population.append(mutate(child2))
        population = sorted(new_population, key=fitness_fn, reverse=True)[:POPULATION_SIZE]
    return population

# Evolve populations for each of the first three algorithms
for i in range(NUM_ALGORITHMS):
    populations[i] = evolve(populations[i], fitness_functions[i])

# Combine the best individuals from each algorithm
def create_hybrid_population(populations, num_best=10):
    hybrid_population = []
    for pop in populations:
        hybrid_population.extend(sorted(pop, key=lambda dna: sum([fn(dna) for fn in fitness_functions]), reverse=True)[:num_best])
    return hybrid_population

hybrid_population = create_hybrid_population(populations)

# Example criteria evolution mechanism
def evolve_fitness_criteria(hybrid_population):
    average_gene = np.mean([np.mean(dna) for dna in hybrid_population])
    if average_gene > 0.5:
        return lambda dna: sum(dna) * 1.1
    else:
        return lambda dna: sum(dna) * 0.9

# Update fitness functions based on new criteria
new_fitness_fn = evolve_fitness_criteria(hybrid_population)
fitness_functions = [new_fitness_fn] * NUM_ALGORITHMS

# Evolve the hybrid population with the new fitness criteria
hybrid_population = evolve(hybrid_population, new_fitness_fn)

# Example of usage in the system
logging.info("Initial populations evolved independently.")
logging.info("Hybrid population created and evolved with new fitness criteria.")

	import os
	import json
	import random
	from sklearn.ensemble import IsolationForest
	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import OneHotEncoder
	from sklearn.neural_network import MLPClassifier
	from deap import base, creator, tools, algorithms
	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torch.nn.functional as F
	import datetime
	import time
	import threading
	import logging
	import multiprocessing
	from collections import deque

	# Logging Configuration
	logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')

	# Memory Model
	class MemoryModel:
	def __init__(self, memory_file='memory.json', max_memory=1000):
	self.memory_file = memory_file
	self.max_memory = max_memory
	self.memory = self.load_memory()

	def load_memory(self):
	if os.path.exists(self.memory_file):
	with open(self.memory_file, 'r') as file:
	return json.load(file)
	return []

	def save_memory(self):
	with open(self.memory_file, 'w') as file:
	json.dump(self.memory, file)

	def add_entry(self, context, response, emotion_state, timestamp=None):
	timestamp = timestamp or datetime.datetime.now().isoformat()
	entry = {
	'timestamp': timestamp,
	'context': context,
	'response': response,
	'emotion_state': emotion_state
	}
	self.memory.append(entry)
	if len(self.memory) > self.max_memory:
	self.memory.pop(0) # Remove the oldest entry
	self.save_memory()

	def retrieve_memory(self, query, context_window=5):
	relevant_entries = [entry for entry in self.memory if query.lower() in entry['context'].lower()]
	if relevant_entries:
	sorted_entries = sorted(relevant_entries, key=lambda x: x['timestamp'], reverse=True)
	return sorted_entries[:context_window]
	return None

	# Temporal Awareness Module
	class TemporalAwareness:
	def __init__(self, context_window=5):
	self.start_time = datetime.datetime.now()
	self.last_event_time = None
	self.event_sequence = deque(maxlen=context_window)
	self.context_window = context_window

	def update_event_time(self, event):
	current_time = datetime.datetime.now()
	if self.last_event_time:
	duration = (current_time - self.last_event_time).total_seconds()
	self.event_sequence.append({
	'event': event,
	'timestamp': current_time.isoformat(),
	'duration_since_last': duration
	})
	else:
	self.event_sequence.append({
	'event': event,
	'timestamp': current_time.isoformat(),
	'duration_since_last': None
	})
	self.last_event_time = current_time

	def estimate_duration(self, event):
	recent_events = list(self.event_sequence)
	durations = [
	seq['duration_since_last'] for seq in recent_events if seq['event'] == event and seq['duration_since_last'] is not None
	]
	return sum(durations) / len(durations) if durations else None

	# HRL Neuron Class
	class HRLNeuron(nn.Module):
	def __init__(self, input_dim, output_dim):
	super(HRLNeuron, self).__init__()
	self.fc1 = nn.Linear(input_dim, 128)
	self.fc2 = nn.Linear(128, output_dim)

	def forward(self, x):
	x = F.relu(self.fc1(x))
	x = self.fc2(x)
	return x

	class HRLAgent:
	def __init__(self, input_dim, output_dim, lr=0.001):
	self.model = HRLNeuron(input_dim, output_dim)
	self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
	self.criterion = nn.MSELoss()

	def act(self, state):
	state = torch.FloatTensor(state)
	q_values = self.model(state)
	return q_values

	def learn(self, state, action, reward, next_state, gamma=0.99):
	state = torch.FloatTensor(state)
	next_state = torch.FloatTensor(next_state)
	reward = torch.FloatTensor([reward])
	action = torch.LongTensor([action])

	q_values = self.model(state)
	next_q_values = self.model(next_state)
	target_q_value = reward + gamma * torch.max(next_q_values)

	loss = self.criterion(q_values[action], target_q_value)

	self.optimizer.zero_grad()
	loss.backward()
	self.optimizer.step()

	# Initialize Example Emotions Dataset
	data = {
	'context': [
	'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm',
	'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated',
	'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated',
	'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic',
	'I am pessimistic', 'I feel bored', 'I am envious'
	],
	'emotion': [
	'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger',
	'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust',
	'disgust', 'optimism', 'pessimism', 'boredom', 'envy'
	]
	}
	df = pd.DataFrame(data)

	# Encoding the contexts using One-Hot Encoding
	encoder = OneHotEncoder(handle_unknown='ignore')
	contexts_encoded = encoder.fit_transform(df[['context']]).toarray()

	# Encoding emotions
	emotions_target = df['emotion'].astype('category').cat.codes
	emotion_classes = df['emotion'].astype('category').cat.categories

	# Train Neural Network
	X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
	model = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
	model.fit(X_train, y_train)

	# Isolation Forest Anomaly Detection Model
	historical_data = np.array([model.predict(contexts_encoded)]).T
	isolation_forest = IsolationForest(contamination=0.1, random_state=42)
	isolation_forest.fit(historical_data)

	# Emotional States
	emotions = {
	'joy': {'percentage': 10, 'motivation': 'positive'},
	'pleasure': {'percentage': 10, 'motivation': 'selfish'},
	'sadness': {'percentage': 10, 'motivation': 'negative'},
	'grief': {'percentage': 10, 'motivation': 'negative'},
	'anger': {'percentage': 10, 'motivation': 'traumatic or strong'},
	'calmness': {'percentage': 10, 'motivation': 'neutral'},
	'determination': {'percentage': 10, 'motivation': 'positive'},
	'resentment': {'percentage': 10, 'motivation': 'negative'},
	'glory': {'percentage': 10, 'motivation': 'positive'},
	'motivation': {'percentage': 10, 'motivation': 'positive'},
	'ideal_state': {'percentage': 100, 'motivation': 'balanced'},
	'fear': {'percentage': 10, 'motivation': 'defensive'},
	'surprise': {'percentage': 10, 'motivation': 'unexpected'},
	'anticipation': {'percentage': 10, 'motivation': 'predictive'},
	'trust': {'percentage': 10, 'motivation': 'reliable'},
	'disgust': {'percentage': 10, 'motivation': 'repulsive'},
	'optimism': {'percentage': 10, 'motivation': 'hopeful'},
	'pessimism': {'percentage': 10, 'motivation': 'doubtful'},
	'boredom': {'percentage': 10, 'motivation': 'indifferent'},
	'envy': {'percentage': 10, 'motivation': 'jealous'}
	}

	# Adjust all emotions to a total of 200%
	total_percentage = 200
	default_percentage = total_percentage / len(emotions)
	for emotion in emotions:
	emotions[emotion]['percentage'] = default_percentage

	emotion_history_file = 'emotion_history.json'

	# Load historical data from file if exists
	def load_historical_data(file_path=emotion_history_file):
	if os.path.exists(file_path):
	with open(file_path, 'r') as file:
	return json.load(file)
	return []

	# Save historical data to file
	def save_historical_data(historical_data, file_path=emotion_history_file):
	with open(file_path, 'w') as file:
	json.dump(historical_data, file)

	# Load previous emotional states
	emotion_history = load_historical_data()

	# Function to update emotions
	def update_emotion(emotion, percentage):
	emotions['ideal_state']['percentage'] -= percentage
	emotions[emotion]['percentage'] += percentage

	# Ensure total percentage remains 200%
	total_current = sum(e['percentage'] for e in emotions.values())
	adjustment = total_percentage - total_current
	emotions['ideal_state']['percentage'] += adjustment

	# Function to normalize context
	def normalize_context(context):
	return context.lower().strip()

	# Function to evolve emotions using genetic algorithm (Hyper-Evolution)
	def evolve_emotions():
	def evaluate(individual):
	ideal_state = individual[-1]
	other_emotions = individual[:-1]
	return abs(ideal_state - 100), sum(other_emotions)

	creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
	creator.create("Individual", list, fitness=creator.FitnessMin)

	toolbox = base.Toolbox()
	toolbox.register("attribute", lambda: random.uniform(0, 20))
	toolbox.register("individual", tools.initCycle, creator.Individual, toolbox.attribute, n=(len(emotions) - 1))
	toolbox.register("ideal_state", lambda: random.uniform(80, 120))
	toolbox.register("complete_individual", tools.initConcat, creator.Individual, toolbox.individual, toolbox.ideal_state)
	toolbox.register("population", tools.initRepeat, list, toolbox.complete_individual)

	toolbox.register("evaluate", evaluate)
	toolbox.register("mate", tools.cxTwoPoint)
	toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
	toolbox.register("select", tools.selTournament, tournsize=3)

	population = toolbox.population(n=100)

	for gen in range(100):
	offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.2)
	fits = toolbox.map(toolbox.evaluate, offspring)
	for fit, ind in zip(fits, offspring):
	ind.fitness.values = fit
	population = toolbox.select(offspring, k=len(population))

	if gen % 20 == 0:
	toolbox.register("mate", tools.cxBlend, alpha=random.uniform(0.1, 0.9))
	toolbox.register("mutate", tools.mutPolynomialBounded, eta=random.uniform(0.5, 1.5), low=0, up=20, indpb=0.2)

	best_ind = tools.selBest(population, k=1)[0]
	return best_ind[:-1], best_ind[-1]

	# Additional Genetic Algorithms
	def evolve_language_model():
	def evaluate_language(individual):
	return random.random(),

	creator.create("FitnessMax", base.Fitness, weights=(1.0,))
	creator.create("LanguageIndividual", list, fitness=creator.FitnessMax)

	toolbox = base.Toolbox()
	toolbox.register("language_gene", lambda: random.randint(0, 1))
	toolbox.register("language_individual", tools.initRepeat, creator.LanguageIndividual, toolbox.language_gene, n=100)
	toolbox.register("language_population", tools.initRepeat, list, toolbox.language_individual)

	toolbox.register("evaluate", evaluate_language)
	toolbox.register("mate", tools.cxTwoPoint)
	toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
	toolbox.register("select", tools.selTournament, tournsize=3)

	population = toolbox.language_population(n=50)

	for gen in range(100):
	offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
	fits = toolbox.map(toolbox.evaluate, offspring)
	for fit, ind in zip(fits, offspring):
	ind.fitness.values = fit
	population = toolbox.select(offspring, k=len(population))

	best_language_model = tools.selBest(population, k=1)[0]
	return best_language_model

	def evolve_emotion_recognition():
	def evaluate_emotion_recognition(individual):
	return random.random(),

	creator.create("FitnessMax", base.Fitness, weights=(1.0,))
	creator.create("EmotionRecognitionIndividual", list, fitness=creator.FitnessMax)

	toolbox = base.Toolbox()
	toolbox.register("emotion_gene", lambda: random.randint(0, 1))
	toolbox.register("emotion_individual", tools.initRepeat, creator.EmotionRecognitionIndividual, toolbox.emotion_gene, n=100)
	toolbox.register("emotion_population", tools.initRepeat, list, toolbox.emotion_individual)

	toolbox.register("evaluate", evaluate_emotion_recognition)
	toolbox.register("mate", tools.cxTwoPoint)
	toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
	toolbox.register("select", tools.selTournament, tournsize=3)

	population = toolbox.emotion_population(n=50)

	for gen in range(100):
	offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
	fits = toolbox.map(toolbox.evaluate, offspring)
	for fit, ind in zip(fits, offspring):
	ind.fitness.values = fit
	population = toolbox.select(offspring, k=len(population))

	best_emotion_recognition = tools.selBest(population, k=1)[0]
	return best_emotion_recognition

	# Evolutionary System Implementation

	DNA_LENGTH = 10 # Example DNA length
	POPULATION_SIZE = 50
	GENERATIONS = 100
	NUM_ALGORITHMS = 3

	# Define the initial DNA structure
	def generate_random_dna():
	return [random.uniform(0, 1) for _ in range(DNA_LENGTH)]

	# Create initial populations for each algorithm
	populations = [[generate_random_dna() for _ in range(POPULATION_SIZE)] for _ in range(NUM_ALGORITHMS)]

	# Example Fitness Functions
	def fitness_function_1(dna):
	return sum(dna) # Simplistic example fitness function

	def fitness_function_2(dna):
	return np.prod(dna) # Simplistic example fitness function

	def fitness_function_3(dna):
	return np.mean(dna) # Simplistic example fitness function

	fitness_functions = [fitness_function_1, fitness_function_2, fitness_function_3]

	# Genetic Operators
	def tournament_selection(population, fitness_fn):
	tournament_size = 5
	selected = random.sample(population, tournament_size)
	selected.sort(key=fitness_fn, reverse=True)
	return selected[0]

	def crossover(parent1, parent2):
	point = random.randint(0, DNA_LENGTH - 1)
	child1 = parent1[:point] + parent2[point:]
	child2 = parent2[:point] + parent1[point:]
	return child1, child2

	def mutate(dna, mutation_rate=0.01):
	return [gene if random.random() > mutation_rate else random.uniform(0, 1) for gene in dna]

	def evolve(population, fitness_fn, generations=GENERATIONS):
	for _ in range(generations):
	new_population = []
	for _ in range(POPULATION_SIZE // 2):
	parent1 = tournament_selection(population, fitness_fn)
	parent2 = tournament_selection(population, fitness_fn)
	child1, child2 = crossover(parent1, parent2)
	new_population.append(mutate(child1))
	new_population.append(mutate(child2))
	population = sorted(new_population, key=fitness_fn, reverse=True)[:POPULATION_SIZE]
	return population

	# Evolve populations for each of the first three algorithms
	for i in range(NUM_ALGORITHMS):
	populations[i] = evolve(populations[i], fitness_functions[i])

	# Combine the best individuals from each algorithm
	def create_hybrid_population(populations, num_best=10):
	hybrid_population = []
	for pop in populations:
	hybrid_population.extend(sorted(pop, key=lambda dna: sum([fn(dna) for fn in fitness_functions]), reverse=True)[:num_best])
	return hybrid_population

	hybrid_population = create_hybrid_population(populations)

	# Example criteria evolution mechanism
	def evolve_fitness_criteria(hybrid_population):
	average_gene = np.mean([np.mean(dna) for dna in hybrid_population])
	if average_gene > 0.5:
	return lambda dna: sum(dna) * 1.1
	else:
	return lambda dna: sum(dna) * 0.9

	# Update fitness functions based on new criteria
	new_fitness_fn = evolve_fitness_criteria(hybrid_population)
	fitness_functions = [new_fitness_fn] * NUM_ALGORITHMS

	# Evolve the hybrid population with the new fitness criteria
	hybrid_population = evolve(hybrid_population, new_fitness_fn)

	# Example of usage in the system
	logging.info("Initial populations evolved independently.")
	logging.info("Hybrid population created and evolved with new fitness criteria.")