When an algorithm exploits, it risks missing out on a better option or failing to adapt to a changing environment. Anyone who ...

Abstract: Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despite these ...

Abstract: Data-driven evolutionary algorithms (DDEAs) have achieved significant success in numerous real-world optimization problems, where exact objective functions and constraint functions do not ...

Google plans an AlphaEvolve rollout through Google Cloud after showing gains in TPU design, Spanner efficiency, genomics, ...

The RECAB (Reducing Energy Consumption in Algorithm Benchmarking) project was awarded with funding from NWO’s Sustainable ...

A new study warns that artificial intelligence may be entering an 'evolvable' phase, where systems replicate, vary, and undergo selection with less human oversight. Researchers outline two scenarios: ...

Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

This repository implement MFEA-II MFEA-II Official Matlab Version. Tested on MTSOO benchmark. This repo could be used as a template or starter code for conducting multitasking optimization on other ...

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In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing ...