IEEE

LLM-Driven Evolutionary Algorithm Selection: Bidirectional Framework and Sampling Paradigm

Abstract: Evolutionary algorithms (EAs) exhibit diverse performance across different optimization problems, making the automated selection of the most suitable EA an important yet challenging task.
Scientific Research Publishing

SymPcNSGA-Testing: A Hybrid Approach to Mitigate Path Explosion in Software Programs ()

To address these shortcomings, we introduce SymPcNSGA-Testing (Symbolic execution, Path clustering and NSGA-II Testing), a ...
IEEE

The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits

Abstract: Evolutionary algorithms make countless random decisions during selection, mutation, and crossover operations. These random decisions require a steady stream of random numbers. We analyze the ...