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Found 10 papers, 4 papers with code
Discovering Effective Policies for Land-Use Planning with Neuroevolution
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change.
EVOTER: Evolution of Transparent Explainable Rule-sets
The approach is evaluated in several prediction/classification and prescription/policy search domains with and without a surrogate.
DIAS: A Domain-Independent Alife-Based Problem-Solving System
A domain-independent problem-solving system based on principles of Artificial Life is introduced.
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures.
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes.
Enhanced Optimization with Composite Objectives and Novelty Pulsation
This paper improves this approach further by introducing novelty pulsation, i. e. a systematic method to alternate between novelty selection and local optimization.
Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential
Artificial Intelligence (AI) technology is rapidly changing many areas of society.
Evolutionary Neural AutoML for Deep Learning
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
Evolving Deep Neural Networks
The success of deep learning depends on finding an architecture to fit the task.
