ідея - Neural Networks - # Neural Architecture Search

Lamarckian Co-Evolutionary Algorithm (LCoDeepNEAT) for Developing Convolutional Neural Networks with Optimized Last Layer Weights

Q: How does the performance of LCoDeepNEAT compare to other NAS methods that utilize gradient information, such as differentiable architecture search (DARTS)?

While the provided text doesn't directly compare LCoDeepNEAT to DARTS, we can infer some insights and limitations: Direct vs. Indirect Gradient Use: LCoDeepNEAT uses gradient information indirectly. It trains the last layer for one epoch, encodes the learned weights into the genotype, and uses them for offspring initialization. This differs from DARTS, which uses gradients to directly optimize architecture parameters in a continuous space, allowing for gradient-based optimization. Computational Cost: LCoDeepNEAT's indirect use of gradients and focus on evolving a small subset of weights likely leads to lower computational cost compared to DARTS. DARTS, while efficient for gradient-based NAS, often requires significantly more resources due to the joint optimization of architecture and weight parameters. Exploration-Exploitation Trade-off: LCoDeepNEAT's evolutionary approach might offer better exploration of the search space compared to gradient-based methods like DARTS, which can get stuck in local optima. However, DARTS, with its direct gradient optimization, might be faster at exploiting promising regions of the architecture space. Missing Direct Comparison: Without a direct comparison on common benchmarks, it's impossible to definitively claim superiority for either LCoDeepNEAT or DARTS. The choice depends on the specific application requirements, available computational resources, and the desired balance between exploration and exploitation. Further research directly comparing LCoDeepNEAT with DARTS and other gradient-based NAS methods is needed to provide a conclusive performance comparison.

Q: Could the concept of co-evolving architecture and weights be extended to other types of neural networks beyond CNNs, such as recurrent neural networks (RNNs) or transformers?

Yes, the concept of co-evolving architecture and weights can be extended to other neural network architectures like RNNs and Transformers. Here's how: RNNs: The challenge lies in defining suitable genetic representations and mutation operators for RNN components like recurrent cells (e.g., LSTM, GRU). Genotype: A graph-based genotype could encode connections between different gates within a cell and across time steps. Mutation: Operators could add or remove connections, alter gate types, or modify the number of hidden units. Weight Co-evolution: Similar to LCoDeepNEAT, evolving weights of specific gates (e.g., the output gate) could be explored. Transformers: The key lies in representing the building blocks of Transformers, such as attention heads and feedforward networks, in an evolvable manner. Genotype: A hierarchical genotype could represent the number of encoder/decoder layers, attention heads per layer, and the structure of feedforward networks within each layer. Mutation: Operators could alter the number of layers, heads, or modify the connections within feedforward networks. Weight Co-evolution: Evolving weights of specific attention heads or layers in the feedforward network could be beneficial. Challenges: Computational Complexity: Co-evolving architecture and weights for RNNs and Transformers, especially for large-scale models, can be computationally expensive. Efficient search space reduction techniques and surrogate models for fitness approximation would be crucial. Representation Design: Devising effective genotype representations that capture the architectural nuances of RNNs and Transformers while being amenable to genetic operators is challenging. Despite the challenges, the potential benefits of finding well-performing architectures tailored to specific tasks make co-evolution of architecture and weights a promising research direction for RNNs and Transformers.

Основні поняття

This paper introduces LCoDeepNEAT, a novel Neural Architecture Search (NAS) method based on Lamarckian genetic algorithms, which co-evolves CNN architectures and their last layer weights to achieve faster convergence and higher accuracy in image classification tasks.

Анотація

Bibliographic Information:

Sharifi, Z., Soltanian, K., & Amiri, A. (2023). Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm. 13th International Conference on Computer and Knowledge Engineering (ICCKE 2023), November 1-2, 2023, Ferdowsi University of Mashhad, Iran.

Research Objective:

This paper aims to address the computational challenges of Neural Architecture Search (NAS) by introducing LCoDeepNEAT, a novel approach that co-evolves CNN architectures and their last layer weights using Lamarckian genetic algorithms.

Methodology:

LCoDeepNEAT utilizes a graph-based genetic algorithm with two populations: 'module' and 'individual'. The 'individual' population represents CNN architectures as directed acyclic graphs (DAGs) with nodes representing modules from a 'module' population. Each module represents a small CNN architecture. LCoDeepNEAT employs Lamarckian evolution, where the final layer weights of evaluated architectures are inherited by offspring, accelerating convergence. The algorithm restricts the search space to architectures with two fully connected layers for classification, further enhancing efficiency.

Key Findings:

LCoDeepNEAT demonstrates superior performance compared to hand-crafted CNNs and several state-of-the-art NAS methods on six benchmark image classification datasets.
The co-evolution of architecture and last layer weights, coupled with the Lamarckian inheritance of tuned weights, leads to faster convergence and improved classification accuracy.
Constraining the architecture search space to architectures with two fully connected layers for classification proves to be an effective strategy for finding optimal solutions efficiently.

Main Conclusions:

LCoDeepNEAT presents a novel and efficient approach for NAS, effectively addressing the computational challenges associated with traditional methods. The integration of Lamarckian evolution and a constrained search space significantly contributes to the algorithm's ability to discover competitive CNN architectures with faster convergence and higher accuracy.

Significance:

This research contributes to the field of NAS by introducing a novel algorithm that effectively balances exploration and exploitation in the architecture search space. The proposed approach has the potential to facilitate the development of more efficient and accurate CNNs for various image classification tasks.

Limitations and Future Research:

While LCoDeepNEAT demonstrates promising results, further investigation into evolving weights beyond the last layer and exploring different search space constraints could lead to even more efficient and accurate architectures. Additionally, applying LCoDeepNEAT to more complex image classification tasks and comparing its performance with a wider range of NAS methods would provide a more comprehensive evaluation of its capabilities.

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Статистика

LCoDeepNEAT achieves a classification error rate of 0.33% on the MNIST dataset, comparable to the best-performing NAS method, psoCNN, at 0.32%.
On the MNIST-BI dataset, LCoDeepNEAT achieves the lowest best error rate of 1.02% and the lowest mean error rate of 1.30%, outperforming sosCNN by 0.66% and 0.38% respectively.
For the MNIST-Fashion dataset, LCoDeepNEAT surpasses all handcrafted methods in terms of error rates, including GoogleNet, AlexNet, and VGG-16.
LCoDeepNEAT achieves a 6.21% error rate on MNIST-Fashion with 1.2 million parameters, compared to SEECNN's 5.38% error rate with 15.9 million parameters, highlighting its ability to balance accuracy and complexity.
The combined strategies of evolving the last layer and using Lamarckian inheritance in LCoDeepNEAT result in a 2% to 5.6% improvement in classification accuracy across all datasets.
Evolving only the last layer weights without Lamarckian inheritance in LCoDeepNEAT still yields a 0.4% to 0.8% increase in classification accuracy per generation.

Цитати

"The last layer is an excellent candidate for evolution due to its unique characteristics."
"This paper introduces LCoDeepNEAT, an instantiation of Lamarckian genetic algorithms, which extends the foundational principles of the CoDeepNEAT framework."
"Our method yields a notable improvement in the classification accuracy of candidate solutions throughout the evolutionary process, ranging from 2% to 5.6%."

Ключові висновки, отримані з

Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm

by Zaniar Shari... о arxiv.org 10-31-2024

https://arxiv.org/pdf/2410.22487.pdf

Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm

Глибші Запити

How does the performance of LCoDeepNEAT compare to other NAS methods that utilize gradient information, such as differentiable architecture search (DARTS)?

While the provided text doesn't directly compare LCoDeepNEAT to DARTS, we can infer some insights and limitations:

Direct vs. Indirect Gradient Use: LCoDeepNEAT uses gradient information indirectly. It trains the last layer for one epoch, encodes the learned weights into the genotype, and uses them for offspring initialization. This differs from DARTS, which uses gradients to directly optimize architecture parameters in a continuous space, allowing for gradient-based optimization.
Computational Cost: LCoDeepNEAT's indirect use of gradients and focus on evolving a small subset of weights likely leads to lower computational cost compared to DARTS. DARTS, while efficient for gradient-based NAS, often requires significantly more resources due to the joint optimization of architecture and weight parameters.
Exploration-Exploitation Trade-off: LCoDeepNEAT's evolutionary approach might offer better exploration of the search space compared to gradient-based methods like DARTS, which can get stuck in local optima. However, DARTS, with its direct gradient optimization, might be faster at exploiting promising regions of the architecture space.
Missing Direct Comparison: Without a direct comparison on common benchmarks, it's impossible to definitively claim superiority for either LCoDeepNEAT or DARTS. The choice depends on the specific application requirements, available computational resources, and the desired balance between exploration and exploitation.
Further research directly comparing LCoDeepNEAT with DARTS and other gradient-based NAS methods is needed to provide a conclusive performance comparison.

Could the concept of co-evolving architecture and weights be extended to other types of neural networks beyond CNNs, such as recurrent neural networks (RNNs) or transformers?

Yes, the concept of co-evolving architecture and weights can be extended to other neural network architectures like RNNs and Transformers. Here's how:

RNNs:  The challenge lies in defining suitable genetic representations and mutation operators for RNN components like recurrent cells (e.g., LSTM, GRU).

Genotype:  A graph-based genotype could encode connections between different gates within a cell and across time steps.
Mutation: Operators could add or remove connections, alter gate types, or modify the number of hidden units.
Weight Co-evolution: Similar to LCoDeepNEAT, evolving weights of specific gates (e.g., the output gate) could be explored.


Transformers: The key lies in representing the building blocks of Transformers, such as attention heads and feedforward networks, in an evolvable manner.

Genotype:  A hierarchical genotype could represent the number of encoder/decoder layers, attention heads per layer, and the structure of feedforward networks within each layer.
Mutation: Operators could alter the number of layers, heads, or modify the connections within feedforward networks.
Weight Co-evolution: Evolving weights of specific attention heads or layers in the feedforward network could be beneficial.
Challenges:

Computational Complexity: Co-evolving architecture and weights for RNNs and Transformers, especially for large-scale models, can be computationally expensive. Efficient search space reduction techniques and surrogate models for fitness approximation would be crucial.
Representation Design:  Devising effective genotype representations that capture the architectural nuances of RNNs and Transformers while being amenable to genetic operators is challenging.
Despite the challenges, the potential benefits of finding well-performing architectures tailored to specific tasks make co-evolution of architecture and weights a promising research direction for RNNs and Transformers.

What are the ethical implications of automating the design of artificial intelligence systems through methods like LCoDeepNEAT, particularly in terms of bias and fairness?

Automating AI design with methods like LCoDeepNEAT raises significant ethical concerns, particularly regarding bias and fairness:

Data-Driven Bias Amplification: NAS methods rely heavily on training data. If the data reflects existing societal biases (e.g., under-representation of certain demographics), the evolved AI systems might inherit and even amplify these biases, leading to unfair or discriminatory outcomes.
Lack of Transparency: The "black box" nature of evolved architectures can make it difficult to understand the decision-making process. This lack of transparency hinders identifying and mitigating bias, potentially leading to unfair treatment without a clear explanation.
Unintended Consequences: Evolving AI systems for complex real-world applications without fully understanding the potential consequences can have unintended negative impacts. For instance, an AI system optimized for maximizing profits in a loan-approval system might inadvertently discriminate against certain groups.
Responsibility and Accountability: When AI systems design themselves, determining responsibility for biased or unfair outcomes becomes challenging. Is it the developers of the NAS method, the users deploying the system, or the system itself? Clear lines of accountability are crucial.
Mitigating Ethical Concerns:

Diverse and Representative Data: Training NAS methods on diverse and representative datasets is crucial to minimize bias in the evolved AI systems.
Transparency and Explainability:  Developing techniques to understand and explain the decision-making process of evolved architectures is essential for identifying and addressing bias.
Fairness-Aware Fitness Functions: Incorporating fairness metrics directly into the fitness function can guide the evolution towards AI systems that prioritize fairness alongside performance.
Human Oversight and Regulation:  Maintaining human oversight in the design and deployment of AI systems, along with establishing clear ethical guidelines and regulations, is crucial to ensure responsible use.
Addressing these ethical implications is paramount to ensure that automating AI design with methods like LCoDeepNEAT leads to fair, unbiased, and beneficial outcomes for all.