cs.NE · 2026-06-11 · No. 20

Neural and Evolutionary Computing, 2026-06-11.

3 new papers in cs.NE. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    SPEA2$^+$: Improved Density Estimation in SPEA2 with Provable Runtime Guarantees

    Duc-Cuong Dang, Andre Opris, Dirk Sudholt

    cs.NE · cs.AI

    The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is a popular and prominent evolutionary algorithm for solving multi-objective optimisation problems. Despite its popularity, theoretical analyses of SPEA2 have only appeared recently. Moreover, these analyses focus exclusively on how SPEA2 handles non-dominated solutions and disregard the algorithmic components responsible for handling dominated solutions. We conduct a first runtime...

    arxiv.org/abs/2606.12382 · PDF

  2. 02

    SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

    Claas Beger, Florian Walter, Alois Knoll

    cs.NE · cs.AI

    The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption. To address this issue, we consider Spiking Neural Networks (SNNs), which are an energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their naturally event-driven approach to processing information....

    arxiv.org/abs/2606.12287 · PDF

  3. 03

    Mathematical perspective on genetic algorithms with optimization guided operators

    Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel

    cs.NE · cs.AI · cs.LG

    Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recombination operators involved are qualitatively different from those studied classically. Mutations are no longer random; an ML algorithm mutates a solution with the goal of improving an objective. Similarly, recombination is not based on random collages of parent solutions. Instead, it is an ML...

    arxiv.org/abs/2606.12279 · PDF

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