Evolution of Evolutionary Computing and Its Interaction with Deep Learning
Exploring the Intersections of Science, Dedication, and the Progress of Multi-Goal Optimization Techniques
Evolutionary computing, a field that includes techniques like genetic algorithms and evolutionary strategies, has been instrumental in solving optimization and search problems in AI. These methods mimic the process of evolution to find optimal solutions by iteratively selecting and adapting the best candidates.
Over the past decade, evolutionary computing has continued to evolve and play a significant role in AI and machine learning. However, its prominence has somewhat been overshadowed by the rapid advancements in neural networks and deep learning models.
In recent years, deep learning models, particularly transformer architectures, have become increasingly dominant in AI and machine learning. Transformer models, such as BERT, GPT, and their derivatives, have revolutionized natural language processing and other tasks by leveraging self-attention mechanisms to process data in parallel.
Despite the rise of transformer models, evolutionary computing continues to find applications in two main ways: 1. Optimization of Deep Learning Models: Evolutionary algorithms can be used to optimize hyperparameters or architectures of deep learning models, improving their efficiency and performance. 2. Hybrid Approaches: Some researchers explore hybrid models that combine evolutionary techniques with deep learning, aiming to leverage the strengths of both. This includes using evolutionary strategies for improving model robustness or adaptation in dynamic environments.
Professor Carlos Artemio Coello Coello, a renowned expert in the field, is particularly fascinated by the relationship between algorithmic and natural evolution. His PhD research in 1996 at Tulane University focused on evolutionary multi-objective optimization, sparked by a paper he read that used genetic algorithms to solve a structural optimization problem.
While evolutionary computing has been fruitful in certain domains, its application has been hesitantly embraced in the multi-objective optimization field. Professor Coello Coello believes that understanding existing tools is essential for real scientific advancement in this area.
Going forward, there is potential for further integration of evolutionary computing with deep learning, particularly in areas where adaptability and optimization are crucial. While transformer models have captured much of the attention in AI, evolutionary computing remains valuable for tasks requiring efficient optimization and adaptation.
In the interview, conducted on behalf of the BNVKI, the Benelux Association for Artificial Intelligence, Professor Coello Coello emphasized the importance of a harmonious coexistence between research by analogy and the cultivation of groundbreaking ideas in fostering a vibrant and adaptive research field.
References: 1. Evolutionary Multi-objective Optimization: A Review 2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 3. GPT-3: Language Models are Few-Shot Learners 4. The Impact of Scale on Learning Algorithms
Science and artificial-intelligence intersect in the ongoing development of hybrid AI models that combine evolutionary computing with deep learning. For instance, evolutionary algorithms can optimize the hyperparameters or architectures of deep learning models, improving their efficiency and performance (science). Meanwhile, researchers are exploring hybrid approaches that leverage the robustness and adaptation capabilities of evolutionary techniques in dynamic environments (artificial-intelligence).
