Neighborhood Adaptive Differential Evolution

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 1–2

Abstract

We introduce Neighborhood Adaptive DE, an automatically designed variant of Differential Evolution (DE) tuned specifically for Many Affine Black-Box Optimization benchmark functions. The algorithm was generated by the Large Language Model Evolutionary Algorithm LLaMEA approach, which employed a (4,12) Evolution Strategy in combination with the large language model Gemini-2.0-Flash to generate new optimization algorithms. Key features of Neighborhood Adaptive DE include sampling a small, randomly chosen subset of 'neighbors' for each individual and using the best among those as the base vector in mutation (instead of the global best). This neighborhood-based strategy, along with adaptive tuning of scale factor F and crossover rate CR, leads to greater diversity and mitigates premature convergence. Additionally, an automatic restart mechanism re-initializes the population upon detecting stagnation. Experimental benchmarking indicates that Neighborhood Adaptive DE outperforms other algorithms on the benchmark, including the winner of last-years Many Affine competition, suggesting that automated design with LLaMEA can yield novel, high-performance algorithms for challenging black-box optimization tasks.

Authors 1

1. Bas van Stein corresponding Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands