We propose a novel type of Artificial Immune System (AIS): Symbiotic
Artificial Immune Systems (SAIS), drawing inspiration from symbiotic
relationships in biology. SAIS parallels the three key stages (i.e., mutualism,
commensalism and parasitism) of population updating from the Symbiotic
Organisms Search (SOS) algorithm. This parallel approach effectively addresses
the challenges of large population size and enhances population diversity in
AIS, which traditional AIS and SOS struggle to resolve efficiently. We
conducted a series of experiments, which demonstrated that our SAIS achieved
comparable performance to the state-of-the-art approach SOS and outperformed
other popular AIS approaches and evolutionary algorithms across 26 benchmark
problems. Furthermore, we investigated the problem of parameter selection and
found that SAIS performs better in handling larger population sizes while
requiring fewer generations. Finally, we believe SAIS, as a novel bio-inspired
and immune-inspired algorithm, paves the way for innovation in bio-inspired
computing with the symbiotic paradigm.

我们提出了一种新型的人工免疫系统：共生人工免疫系统（SAIS），受到生物学中共生关系的启发。SAIS 采用了共生有机体搜索（SOS）算法的三个关键阶段（互利共生、寄生共生和共生共生）的并行方法，从而有效地解决了人工免疫系统中存在的大群体规模和种群多样性问题，这是传统人工免疫系统和 SOS 难以有效解决的。我们进行了一系列实验，结果表明我们的 SAIS 在 26 个基准问题上达到了与最先进的 SOS 方法相当的性能，并且在其他广泛应用的人工免疫系统方法和进化算法上表现出色。此外，我们研究了参数选择问题，并发现 SAIS 在处理更大的种群规模时表现更好，同时需要更少的世代。最后，我们相信 SAIS 作为一种新颖的生物和免疫启发算法，为共生范式中的生物启发计算的创新铺平了道路。

SAIS: 基于共生范式的一种新型生物启发式人工免疫系统

SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic  Paradigm

Current Neural Architecture Search techniques can suffer from a few
shortcomings, including high computational cost, excessive bias from the search
space, conceptual complexity or uncertain empirical benefits over random
search. In this paper, we present ImmuNeCS, an attempt at addressing these
issues with a method that offers a simple, flexible, and efficient way of
building deep learning models automatically, and we demonstrate its
effectiveness in the context of convolutional neural networks. Instead of
searching for the 1-best architecture for a given task, we focus on building a
population of neural networks that are then ensembled into a neural network
committee, an approach we dub 'Neural Committee Search'. To ensure sufficient
performance from the committee, our search algorithm is based on an artificial
immune system that balances individual performance with population diversity.
This allows us to stop the search when accuracy starts to plateau, and to
bridge the performance gap through ensembling. In order to justify our method,
we first verify that the chosen search space exhibits the locality property. To
further improve efficiency, we also combine partial evaluation, weight
inheritance, and progressive search. First, experiments are run to verify the
validity of these techniques. Then, preliminary experimental results on two
popular computer vision benchmarks show that our method consistently
outperforms random search and yields promising results within reasonable GPU
budgets. An additional experiment also shows that ImmuNeCS's solutions transfer
effectively to a more difficult task, where they achieve results comparable to
a direct search on the new task. We believe these findings can open the way for
new, accessible alternatives to traditional NAS.

本文介绍一种名为 ImmuNeCS 的神经委员会搜索方法，该方法使用人工免疫系统平衡个体表现和种群多样性，从而使在合理的 GPU 预算内，该方法在计算机视觉基准上始终优于随机搜索，并显示出良好的结果。

ImmuNeCS: 一种基于人工免疫系统的神经委员会搜索

ImmuNeCS: Neural Committee Search by an Artificial Immune System

We present a time complexity analysis of the Opt-IA artificial immune system
(AIS). We first highlight the power and limitations of its distinguishing
operators (i.e., hypermutations with mutation potential and ageing) by
analysing them in isolation. Recent work has shown that ageing combined with
local mutations can help escape local optima on a dynamic optimisation
benchmark function. We generalise this result by rigorously proving that,
compared to evolutionary algorithms (EAs), ageing leads to impressive speed-ups
on the standard Cliff benchmark function both when using local and global
mutations. Unless the stop at first constructive mutation (FCM) mechanism is
applied, we show that hypermutations require exponential expected runtime to
optimise any function with a polynomial number of optima. If instead FCM is
used, the expected runtime is at most a linear factor larger than the upper
bound achieved for any random local search algorithm using the artificial
fitness levels method. Nevertheless, we prove that algorithms using
hypermutations can be considerably faster than EAs at escaping local optima. An
analysis of the complete Opt-IA reveals that it is efficient on the previously
considered functions and highlights problems where the use of the full
algorithm is crucial. We complete the picture by presenting a class of
functions for which Opt-IA fails with overwhelming probability while standard
EAs are efficient.

本研究对 Opt-IA 人工免疫系统进行了时间复杂度分析，并证明了与进化算法相比，使用老化操作可以提高标准基准函数的计算速度。