# Fast Artificial Immune Systems

arXiv: https://arxiv.org/abs/1806.00299 · HTML: https://mlsanity.com/paper/1806.00299/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Lower Bounds from Fitness Levels Made Easy](https://mlsanity.com/paper/2104.03372/) (2021)
- [On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best](https://mlsanity.com/paper/2103.10394/) (2021)
- [An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics](https://mlsanity.com/paper/2105.03090/) (2021)
- [When Non-Elitism Meets Time-Linkage Problems](https://mlsanity.com/paper/2104.06831/) (2021)
- [Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise](https://mlsanity.com/paper/2305.04553/) (2023)

## Abstract (arXiv)

Various studies have shown that characteristic Artificial Immune System (AIS) operators such as hypermutations and ageing can be very efficient at escaping local optima of multimodal optimisation problems. However, this efficiency comes at the expense of considerably slower runtimes during the exploitation phase compared to standard evolutionary algorithms. We propose modifications to the traditional `hypermutations with mutation potential' (HMP) that allow them to be efficient at exploitation as well as maintaining their effective explorative characteristics. Rather than deterministically evaluating fitness after each bitflip of a hypermutation, we sample the fitness function stochastically with a `parabolic' distribution which allows the `stop at first constructive mutation' (FCM) variant of HMP to reduce the linear amount of wasted function evaluations when no improvement is found to a constant. By returning the best sampled solution during the hypermutation, rather than the first constructive mutation, we then turn the extremely inefficient HMP operator without FCM, into a very effective operator for the standard Opt-IA AIS using hypermutation, cloning and ageing. We rigorously prove the effectiveness of the two proposed operators by analysing them on all problems where the performance of HPM is rigorously understood in the literature. %