Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
arXiv:2606.10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study. We demonstrate how deep-RL can be leveraged to overcome this barrier, using the (1+($\lambda$,$\lambda$))-genetic algorithm optimizing OneMax, one of the few problems where a super-constant speedup of dynamic
The increasing complexity of AI systems, particularly evolutionary algorithms, necessitates more sophisticated and interpretable control mechanisms, which deep-RL is now capable of providing.
This research provides a pathway to making highly complex AI systems more understandable and controllable, which is crucial for their deployment in critical applications and for accelerating their development.
The ability to discover interpretable multi-parameter control policies using deep-RL for evolutionary algorithms marks a step towards more transparent and efficient AI optimization.
- · AI researchers
- · Developers of complex optimization systems
- · Industries relying on evolutionary algorithms
- · Developers of black-box optimization algorithms
Deep-RL becomes a more powerful tool for designing and optimizing other AI systems by improving meta-parameters.
Improved interpretability could accelerate AI adoption in highly regulated sectors due to better auditability.
The principles of interpretable multi-parameter control could extend to other complex adaptive systems beyond AI, enhancing their design and management.
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Read at arXiv cs.LG